case study for organic farming

Agriculture in India

Case studies on organic agriculture in india.

Here is a compilation of case studies on organic agriculture in the different regions of India.

1. Case Study on Organic Agriculture in North India:

Himachal Pradesh, Punjab, and Uttaranchal Integrated Watershed Development Project:

The Shiwalik Hills stretch into five states- Jammu and Kashmir, Punjab, Himachal Pradesh, Haryana, and Uttaranchal. These foothills of the Himalayan range have been identified as one of the degraded agro-ecosystems of India facing acute shortages of drinking water and deforestation to meet fodder and fuel requirements.

Poverty in the region is further compounded by poor infrastructure that keeps areas isolated. An Integrated Watershed Development Project (IWDP) was launched by the government with World Bank support to improve the production potential of the area by evolving the watershed management technologies and encouraging community participation.

The project includes an ecologically-friendly or organic farming component designed to play a vital role in several ways –  to restore the fragile agro-ecosystem in the watershed development area; to minimise the impact of agricultural activities on the environment; and to increase farmers’ income. The states of Uttaranchal, Himachal Pradesh and Punjab have made a start in organic agriculture and their projects are reviewed.

The entire project covers 835 villages with an area of 103652 ha. Farmers throughout are small and typically marginal with land holdings 0.2 to 1 ha. Many plots are in higher elevations and on steep slopes throughout the watershed area. Most of the farmers use traditional methods with the more recent advent of conventional components such as chemical fertilizers and pesticides, with the latter having become particularly common.

With little information or visible proof, many farmers are not convinced about the potential of organic methods. Adoption has therefore been fragmented, slow, and partial. Most fear a reduction in yields and difficulties with pest management. Since the entire structure and network of public information has long been geared toward efforts to adopt agro-chemical technologies, most extension agents are unprepared and often not wholly convinced.

Consequently, extension services advocate Integrated Pest Management (IPM) wherein insecticides are suggested as a last resort. Where organic farming is adopted, it is primarily appreciated for the substitution of costly chemical fertilizers.

The project has developed implementation units to help create awareness of organic methods through trainings, demonstrations visits, and interactive workshops. The concept of a bio-villages — where farmers are concerned with natural resource conservation and have adopted the organic farming—has been introduced and a number of these have been constituted although ecological and organic practices are only beginning.

There are few effective farmers’ organisations to help further this work and local governments have in some cases recruited NGOs to help them. For the extension agents, these concepts are novel and many lack of the training and knowledge of organic standards and certification.

A variety of crops such as ginger, peas, capsicum, wheat, paddy and seasonal vegetables are cultivated in the region. Since the land holdings are small, many farmers have very little marketable surplus. Most of the production, remains in the region, being sold in the local market and going to the towns and urban centres. In few villages, farmers pool their produce and hire a truck and sell the produce about 100 kilometres away from the village to get a better price.

Uttaranchal (Millet, Rice, and Beans):

Uttaranchal is a border state in India’s mountainous northwest region where agriculture is the primary form of both subsistence and income. Part of the organic focus is on ten mountainous districts and three in the plains areas. The farmers in the hill regions are often poor and marginal.

The land holdings under organic farming in various organic projects range between 0.1 ha to 5 ha. In many cases organics has first been targeted for adoption among the poorest and thus organic farmers tend to have land area that is three to five times smaller than their conventional neighbours. In the mountainous areas women play a very important role in agriculture. To a large extent men plough the land, while women carry out most other operations like planting, weeding, fertilisation, and harvesting.

In the State of Uttaranchal, organic agriculture is being given an impetus by the state government that has officially declared Uttaranchal as an ‘organic state’. The Government of Uttaranchal is implementing policies that would encourage and incorporate organic methods in all government supported endeavors.

This includes research, training of extension services, incentives, and marketing and promotion. There are at least five major projects currently underway that incorporate various organic components such as composting and biodynamic. Government commitment has extended to rural youth training programmes and the concept of bio-villages has been adopted and promulgated in several areas.

To facilitate coordination and promotion of organic agricultural activities in the state, in July 2003 the Uttaranchal Organic Commodity Board (UOCB) was formed. In 2004, 475 villages with 7125 farmers are involved in the organic agriculture projects of the state.

Self-help groups and village level organisations play some role in the development and dissemination of organics, but public agencies, i.e. extension services are still predominant. In several cases, these have integrated with specialised NGOs to help improve the uptake of improved compost and other organic methods.

For farmers involved in the more marketable crops such as basmati rice and kidney beans, a group certification process has been undertaken in order to reduce costs and improve the adherence to organic standards. For the most part, farmer groups are not as prominent in the organic process. The products produced under the various initiatives are mainly commodities. These are led by finger millet, kidney beans, and rice but also include wheat, maize, ginger, soybeans and several pulses.

Marketing efforts have been focused primarily at the domestic level. Through direct contact and participation in trade fairs and exhibitions, modest sales have been generated. In some cases these sales are for the domestic market and in other cases, traders export them. The state has planned to develop 33 marketing centres for organic products and one has already opened for business. Thirteen tons of organic rice has also been directly exported.

2. Case Study on Organic Agriculture in West India:

Maharashtra (Sorghum, Wheat, and Cotton):

Much of Maharashtra’s Aurangabad region is considered to be very poor. Agriculture is the main source of income and the area depends on modest rainfall that is concentrated in the summer months. The area is multicultural with a sizable Muslim minority comprising approximately 35% of the total population.

The average farm holding is small, between 0.4 and 2 ha with the largest farmers reaching 4 ha. Production methods in the region are a mixture of traditional unconventional, but poverty levels have dictated rather modest use of synthetic agro- chemical inputs.

The Institute for Integrated Rural Development (IIRD) is a civic organisation that has targeted women, and particularly destitute women, for training needs and rural development activities. As a result, 60% of its beneficiaries are women. Accordingly, it is also women who facilitate and organise local groups.

These in turn are supported by technical staffs from IIRD who provide the inputs and the training required. The current project began with 400 farmers in 1992. Today, it has grown to over 1700 farmers. IIRD’s innovations and success have led it to develop training programmes for other NGOs and for public officials.

Organic agriculture has taken an increasing role since the mid-1990s. Although IIRD remains a central fulcrum, many of the project activities are increasingly taken up by the layers of organised farmers that have been developed as part of the project’s empowerment and sustainability goals. IIRD continues to provide on-farm support, certification and marketing services. The farmers are not externally certified but they have an internal certification system in place.

Food security was a predominant concern for a number of years and the focus crops included cereals, legumes, oilseeds, and spices. More recently, as food security has improved, marketing has emerged as a prime concern. The main organic products grown are wheat, sorghum, cotton, and pearl millet.

IIRD has established a weekly organic bazaar in the city of Aurangabad to foster more direct linkages between producers and consumers as well as providing a consistent platform for the exchange of products and services related to organic farming. The bazaar now sells approximately 40% of farmers’ marketable surplus. The rest is sold to local traders and markets.

3. Case Study on Organic Agriculture in South India:

Kerala (Spices and Banana):

The Idukki District is part of Kerala’s Western Ghats region, recognised as one of the world’s 25 bio-diversity hot spots. This hilly region receives adequate rainfall and has maintained a considerable amount of forest cover despite increasing threats from agriculture and timber interests. Three systems of production have dominated the project area. On steep slopes small farmers cultivate multiple crops.

In some valley areas, companies own vast tea plantations. Around such plantations, marginal ethnic farmers cultivate tea in isolated small patches. Except for cardamom, use of pesticide is minimal among small and marginal farmers. The corporate farming enterprises reportedly use considerable quantities of both pesticides and chemical fertilizers.

Currently 1667 certified organic farmers are cultivating one of the areas major products; spices. These cover 1487 ha and none of the farmers own more than one hectare of land. Among them, 1411 farmers are certified through active participation of a local charitable organisation, and 258 farmers are certified through the financial support of the Spices Board, an autonomous agency of the Government of India. The Tea project involves 1200 farmers, cultivating 1110 ha as smallholdings.

Peermade Development Society (PDS) emerged as an NGO in 1980 and as a social service wing of a Christian diocese in response to extensive agrochemical contamination in the area’s drinking water. This resulted in acute toxicity of farmers in the region leading to their hospitalisation. It has focused on tribal and marginalised farmers and contributed to the development of farmer led organisations for the dissemination of organic practices and to effect quality control and standards compliance.

At the field level, farmers are organised into Self-Help Groups (SHGs) with additional layers of organisation that manage local agricultural development. PDS has invested considerable efforts with its participating farmers to develop empowering mechanisms and procedures that prevent domination and subordination patterns that have proved to be detrimental for farmers in the region.

It has developed farmer-led regulatory mechanisms to promote compliance with organic standards that farmers perceive more as a farm management tool to improve their processes and efficiencies.

PDS also links with government and other organisations to promote sustainable farming methods, to conduct joint research with farmers, to control pests and diseases, to facilitate value-added processing and to promote and prove the production of biological inputs for farming. Several of its units such as the Awareness Building Group or Training Centre serve to develop new forms of enterprise and a Land to Lab Centre encourages farmer-oriented innovation and testing of ideas.

PDS has established processing facilities for the farmers to capture more value and its Export Division is one of several functional marketing units that export primarily pepper and bananas. Most of the production is destined for the domestic market and an integral part of PDS’ success has been its entrepreneurial experience. It helped to develop the local medicinal plants industry that integrated with pharmaceutical processing and national as well as overseas marketing.

Spices and tea are the primary crops but several other varieties of nuts and fruits are also produced. In keeping with the project biodiversity commitment, no cereals are cultivated and diverse tree fruits are encouraged. These include jackfruit, banana, plantains, coconut, and guava. Most are for self-consumption as are the few vegetables and greens cultivated by many households.

Karnataka (Vanilla, Pepper, Banana, Rice and Sugar):

The Mandya District, just east of Mysore, falls into the Southern Dry Zone with an annual rainfall of 700 mm. The farmland is very dry. The 1288 beneficiary families in the surrounding village depend mainly on agriculture for their livelihood. All are poor and literacy is lower than the Indian average (males 83% and women 35%). Nearly 90% are small and marginal farmers with an average land holding of less than I ha. The total cultivable area is 2129 ha of which nearly 1655 ha (approximately 80%) are under irrigation.

Until the mid-90s most farmers practiced chemically- oriented farming primarily with mono-crops such as rice, sugarcane, and pulses. Many have abandoned mono- cropping and synthetic agro-chemicals and some have attained organic certification. Crop diversification and related activities have increased the average income of farmers by 25%.

The Eco-Agri Research Foundation (EARF) — as the central organising body — is a registered trust that since 1994 serves as a 50 ha model farm demonstrating organic farming and biodynamic practices. The concept demonstrates a complex system of eco-farming with- animal husbandry; conservation of soil and water through water harvesting structures; vegetative soil erosion checks; and production of high value crops like vanilla, pepper, and banana.

Its purpose is to show the types of ‘Farming Systems’ suitable for the area and promote the concept of a land and cattle-based economy that is in harmony with nature. The main objectives were to create models of sustainability through adoption of organic and bio-dynamic practices and to demonstrate such models to the farmers of surrounding areas. Its local presence has permitted practical field testing of the new approaches and this has reduced farmers’ trepidation to adopt new methods.

The difficulties experienced in the conversion to organic/bio-dynamic farming and in the marketing of the produce by the farmers in the area resulted in a number of useful lessons that resulted in EARF taking on some ‘social entrepreneur’ responsibilities since 1996.

The major organic products generated are jaggery sugar (54 mt), rice (25 mt), vanilla (0.8mt) and banana (10 mt); much of which is sold by the EARF in domestic markets at nearby Bangalore and Mysore and to market agents in other major cities.

The Foundation pays farmers a substantial premium, even during the transition. It is also procuring organically grown vanilla, ginger and pepper from other parts of Karnataka as well as from the neighbouring state of Kerala to combine with its own and improve export efficiencies to the USA and European countries.

4. Case Study on Organic Agriculture in Central India:

Madhya Pradesh (Cotton):

Maikaal bioRe is an initiative situated in a traditional cotton growing area, which mainly extends along the flat topography of the Narmada River in the Khargone District. This many case study results from a more extensive research project monitoring input, output and field data of 100 organic and conventional farms over the complete cropping period 2003/04. The farms were selected on a random basis in ten randomly selected villages of the Maikaal project region. The selected organic farms have been in the project for at least three years.

By the early 1990s companies in cotton business had become acutely aware of declining yields, deterioration of soil fertility and persistent pollution from the increasing necessity to apply pesticides (In India, cotton is grown on 5% of the cultivable land, but receives 54% of the insecticides used in agriculture). The same problems were occurring in many of the other major cotton producing countries as well.

In 1993, the Maikaal organic cotton initiative was started by a major Swiss yarn trading company, together with Maikaal Fibres Ltd., an Indian spinning mill. The experiment developed into a commercial project, which has grown and is now run by an independent company called Maikaal bioRe (India) Ltd, employing 36 persons.

Farmers are both suppliers (raw cotton) to the company and its customers for support services such as training, consulting, crop monitoring, inputs, etc. Two farmers already sit on the Board as Directors and the company’s intention is to involve more as shareholders.

The project focuses on biodynamic, certified organic cotton for the export market. It demonstrates how strong corporate leadership can create mutually profitable initiatives that address the environmental needs of farming communities. The project has a strong market orientation and has helped farmers to efficiently apply state-of the- art organic technology and methods.

Training is an integral part of participation and the company provides all necessary inputs. This results in considerable efficiencies. For example, labour utilisation is less in the organic systems than in the conventional system. Production costs are lower and yields are higher than in similar conventional systems.

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• Published: 22 October 2019

The greenhouse gas impacts of converting food production in England and Wales to organic methods

• Laurence G. Smith   ORCID: orcid.org/0000-0002-9898-9288 1 , 2 ,
• Guy J. D. Kirk   ORCID: orcid.org/0000-0002-7739-9772 1 ,
• Philip J. Jones   ORCID: orcid.org/0000-0003-3464-5424 3 &
• Adrian G. Williams 1  

Nature Communications volume  10 , Article number:  4641 ( 2019 ) Cite this article

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• Agroecology
• Agriculture
• Environmental impact

Agriculture is a major contributor to global greenhouse gas (GHG) emissions and must feature in efforts to reduce emissions. Organic farming might contribute to this through decreased use of farm inputs and increased soil carbon sequestration, but it might also exacerbate emissions through greater food production elsewhere to make up for lower organic yields. To date there has been no rigorous assessment of this potential at national scales. Here we assess the consequences for net GHG emissions of a 100% shift to organic food production in England and Wales using life-cycle assessment. We predict major shortfalls in production of most agricultural products against a conventional baseline. Direct GHG emissions are reduced with organic farming, but when increased overseas land use to compensate for shortfalls in domestic supply are factored in, net emissions are greater. Enhanced soil carbon sequestration could offset only a small part of the higher overseas emissions.

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Introduction.

Organic farming is often suggested as a solution to the negative environmental effects of current food production 1 . Reduced farm inputs and more soil carbon sequestration may alter local GHG budgets favourably. But this must be set against the need for increased production and associated land conversion elsewhere as a result of lower crop and livestock yields under organic methods.

Past studies of the potential of organic farming to mitigate GHG emissions have produced mixed results 2 . For example, Williams et al. 3 found that most organic cropping systems in England generate similar or greater GHG emissions per tonne of crop compared with conventional systems, with lower yields and increased rates of nitrate leaching offsetting the lower use of inputs. Conversely, a Swiss study, which considered entire crop rotations and less-intensive modes of production than Williams et al. 3 , found much lower GHG emissions per tonne of organic crop 4 . Studies comparing organic and non-organic livestock production have also yielded mixed results. In dairy production, reduced use of inputs per tonne of milk under organic management is offset by lower milk yields and lower feed conversion ratios 3 , 5 . Whereas organic beef and sheep production systems can have greater environmental efficiencies as a result of the replacement of manufactured nitrogen (N) fertiliser with biologically-fixed N from forage legumes 6 , 7 , 8 . In organic poultry production, reduced productivities and low feed conversion ratios considerably reduce environmental efficiencies 9 , 10 , 11 . Similarly, organic pig production tends to have lower environmental efficiencies per tonne of product due to lower stocking densities and less output per hectare 12 , 13 . Even where environmental efficiency per hectare is improved, organic systems require more land per tonne of product as a result of lower yields: Williams et al. 3 found additional land requirements of from 65 to 200%.

The most recent attempt to quantify the GHG mitigation potential of organic farming at a national scale was made by Audsley et al. 14 , who used a life-cycle assessment model (LCA) to compare UK organic and conventional data on commodity production, processing, distribution, retail and trade. A ‘baseline’ LCA based assessment, reflecting actual consumption patterns, was compared with a range of scenarios, one of which was a transition to 100% organic production. This built on a study by Jones and Crane 15 in which the production impacts of a 100% conversion to organic agriculture in England and Wales were estimated using data on organic yields, crop areas and livestock numbers from the Farm Business Survey. The results indicated that a switch to organic production in the UK could result in a GHG emission reduction of about 8% in terms of UK production. However, the emissions associated with the additional land use changes overseas required to meet UK supply shortfalls were not considered.

In an earlier study 16 , we developed a model to estimate potential maximum food production from all agriculture—crops and livestock—in England and Wales under organic management. In this paper we extend this analysis to estimate effects on national GHG balances. We assess the impacts of conversion of all agriculture to organic farming using the Agri-LCA models developed by Williams et al. 3 to estimate GHG emissions from individual agricultural systems. This includes carbon dioxide (CO 2 ) emissions from fossil energy use in farm operations and in the production and transport of farm inputs and outputs, as well as emissions of methane (CH 4 ) and nitrous oxide (N 2 O) as functions of soil conditions, nutrient management and livestock variables (Methods). We improved on the Audsley et al. 14 assessment by also accounting for, first, limits to organic production imposed by the supply of livestock feed, rotational constraints and available N, second, the GHG impact of overseas land use changes associated with increased food-imports, and third, the GHG offset potential of soil carbon (C) sequestration under organic production. We also estimate uncertainties in our calculations using Monte Carlo analyses. In doing so we provide the most comprehensive national-scale assessment to-date of the potential land use, production and GHG impacts of up-scaling organic agriculture.

Predicted food production

We predict a drop in total food production expressed as metabolisable energy (ME) by of the order of 40% compared to the conventional farming baseline (Fig.  1 , Supplementary Table  1 ). Human edible protein outputs decreases by a similar proportion (Supplementary Table  2 ). The decrease is due to smaller crop yields per unit of land area under organic management, and the need to introduce fertility-building grass leys with nitrogen-fixing legumes within crop rotations. The latter requirement is a farming system-level effect that is not captured in crop-level comparisons 16 , 17 , 18 .

Projected food production under conventional and organic farming methods. a Crop production and areas. *oilseed rape. b Livestock production and numbers. **sheep numbers × 10, ***poultry numbers × 100 , ****milk production in Mt × 10 5 . Conversion to 100% organic methods caused decreases in wheat, barley, oilseed rape, pigs, eggs, poultry meat and milk, and an overall decrease to 64% of the conventional baseline. Data of Smith et al. 16 . Source Data are provided as a Source Data file

Figure  1 also shows large shifts in the combination of crops grown and numbers of animals reared. Increased diversity of crop rotations under organic management means total vegetable production is maintained 16 . Edible protein production increases in arable areas, particularly in the east and north east of England, through increases in ruminant livestock and legume production 16 . Production of organic oilseed rape (OSR) decreases substantially, primarily because of a much smaller cultivated area due to the relatively low yield of organic OSR compared to both conventional OSR and organic alternatives. The increase in legume and potato production is a result of an increase in the cultivated area: legumes for biological N fixation and potatoes both for weed control and because of their high ME yield. The area would have increased further had the constraint on maximum production in the model not been reached, which we set at 150% of current supply to reflect limits on consumer demand 19 , 20 . Total sugar beet production decreased, but, due to its high ME yield, it reached its upper local limit in parts of eastern England, which we imposed to restrict expansion away from major processing centres 16 . For most crops, the projected decreases in output are considerably greater than might be expected solely from the displacement of crops with leys in organic rotations. The production of minor cereals, such as oats and rye, increases, but this is not sufficient to offset the losses of wheat and barley.

Numbers of grazing livestock (sheep and beef cattle less dairy) increase, because of the increase in feed availability from leys. But the volume of meat produced did not increase in proportion, as a result of lower carcass weights and longer finishing times under organic management. Numbers of monogastric livestock (pigs and poultry) and associated meat production fell sharply as a result of lower stocking rates and availability of concentrated feed. Dairy cattle numbers and milk production decrease due to greater reliance on concentrated feeds than grazing livestock and hence greater sensitivity to N availability, cropping area and cereal yields.

GHG emissions per unit production

Figure  2a shows estimated GHG emissions per unit of production for individual crops. The lower GHG emissions under organic cropping are largely due to replacement of N fertiliser with biological N fixation in leys, resulting in less CO 2 and N 2 O from fertiliser manufacture and less N 2 O per unit of production 3 , 4 , 21 . We concentrate on N in our analysis, and not on other plant nutrients, because N is required in the greatest quantities and its inputs and outputs are the most sensitive to differences between conventional and organic systems. However, balances of P, K and other nutrients must also be maintained, and we therefore account for the GHGs associated with extracting and applying the P and K minerals commonly used in organic systems to maintain balances.

GHG emissions per unit production under conventional and organic farming methods. a Crops. b Livestock, including emissions in feed production. N 2 O = nitrous oxide, CH 4  = methane, CO 2  = carbon dioxide. Production is expressed in tonnes (t) of total metabolised energy. Data are means ±1 standard deviations from the uncertainty analysis (Methods). Emissions due to land use change overseas to compensate for shortfalls in home production, and enhanced soil carbon sequestration under organic methods, are not allowed for. Organic dairy, beef and sheep production have lower total GHG emissions per tonne of product, although greater forage intake increases CH 4 emission. Less N fertiliser use in organic farming gives N 2 O and fossil energy use savings per tonne of product. Exceptions are crops receiving less N fertiliser in conventional farming (beans, oats), organic crops requiring flame weeding (carrots) and organic vegetable crops with lower marketable yields (potatoes, onions). Source Data are provided as a Source Data file

Emissions per unit production are greater for some organic crops, such as field beans, due to increased N leaching and nitrification-denitrification losses, because more must be grown on heavy wet soils. However, a large proportion of field beans grown would have to be exported because of low rates of domestic consumption, and we allow for this in the model with a maximum limit on production, as for potatoes. Oats and spring barley, which require less manufactured N fertiliser than other cereals, have greater GHG emissions per unit production under organic management because yields are smaller. Lower marketable yields in organic potato cropping also lead to greater emissions per unit of product 22 . Emissions are also greater for organic crops requiring higher fossil fuel input in their cultivation, such as organic carrots requiring flame weeding.

Figure  2b shows emissions per unit of production for individual livestock types. Organic pig production results in lower GHG emissions per unit of production because outdoor organic systems use less fossil energy in housing and there are no CH 4 emissions from slurry storage; however, N 2 O emissions increase as a result of greater leaching and denitrification from organic manures. In common with previous studies, we find that poultry meat and egg production generates greater emissions under organic management due to poorer feed conversion ratios, longer rearing times, higher mortality rates and greater leaching losses compared to conventional free range and fully housed systems 9 , 10 . Organic dairy, beef and sheep production results in lower total GHG emissions per unit of production, as a result of the increased efficiency of forage production under organic management, although greater forage intake increases the total CH 4 contribution.

National GHG emissions

Figure  3 gives the aggregated national emissions. It shows that the direct emissions associated with organic crop (Fig.  3a ) and livestock (Fig.  3c ) production are smaller for organic farming compared with conventional: by 20% for crops, 4% for livestock and 6% overall. This is a slightly lower estimate of the effect of conversion to organic farming than in Audsley et al.’s study 14 . The decrease occurs despite an increase in transport emissions, illustrating the relatively small contribution that transport makes to agriculture’s total GHG budget 23 .

Total GHG emissions from food production for England and Wales (E & W) under conventional and organic farming methods. a For food crops for human consumption both from home and overseas production. b Additional net emissions due to soil C sequestration (CS) and overseas land use changes (LUC) to compensate for shortfalls in home production: High = all LUC by conversion from grassland, no CS; Medium = 50% of LUC by conversion from grassland, moderate CS; Low = 25% of LUC by conversion from grassland, high CS; COC= carbon opportunity cost of Searchinger et al. 35 (Methods). c For livestock both from home and overseas production, including emissions during production of crops fed to livestock. d Additional net emissions due to CS and overseas LUC to compensate for shortfalls in livestock production: High, Medium, Low as for Crops. Note LUC losses and CS gains both only apply over the first few decades following conversion, however a flat rate is applied here. Data are means ±1 standard deviation from the uncertainty analysis (Methods). Source Data are provided as a Source Data file

However, the picture is very different when we allow for, first, CO 2 emissions from land use change overseas to make up for shortfalls in home production under organic methods, and second, enhanced soil C sequestration under organic methods at home and overseas, as shown in Fig.  3b , and 3d for different ways of making these allowances. The next two sections give our rationale for how we have done this.

Soil carbon sequestration

Carbon sequestration rates are expected to be greater under organic farming because of greater use of manures and slurry linked to more integrated management of livestock and crops, and longer crop rotations with leys involving forage legumes 24 . Although in conventional systems there is generally a greater separation of livestock from crops, farmyard manures will mostly be applied to land somewhere, so the net transfer of C from the atmosphere to land would be about the same 25 , 26 . On the other hand, excessive manure applications in livestock-dense areas under conventional management leads to over-fertilisation and suboptimal C sequestration 27 . Although we found livestock production decreased under organic management, total livestock numbers were not much different and there was a substantial shift to grazing animals with 61% more sheep and 14% more cattle (beef plus dairy; Fig.  1 ). We estimate there would be approximately 12% more farmyard manure as a result (Supplementary Table  3 ).

We estimate potential C sequestration under organic management using rates of change in soil C derived from the National Soil Inventory of England and Wales for different land use classes by Kirk and Bellamy 28 , and assuming the change from conventional to organic farming was equivalent to a change from continuous arable cropping to rotational grass (Methods). This gives sequestration rates of 0.28 Mg C ha −1  yr −1 for arable land converted to rotational grass, or, after adjusting for the proportion of arable to arable plus rotational grass across England and Wales, 0.18 Mg C ha −1  yr −1 . We used this as the upper rate in the calculations for Fig.  3 . For comparison, in a literature review of experiments comparing conventional and organic farming, Gattinger et al. 24 found sequestration rates between 0.07 and 0.45 Mg C ha −1  yr −1 . However, most of these comparisons involved very high rates of external organic matter inputs to the organic systems, up to 4 times those under conventional farming 26 . Given that we found only 12% more farmyard manure under organic farming, Gattinger et al.’s higher estimates are unrealistic. We therefore use Gattinger et al.’s 24 lower value as the moderate rate in Fig.  3 .

It should be noted that the bulk of any C sequestration will be limited to the first decade or two following conversion, because any given soil has a finite capacity to accumulate C depending on its characteristics and local environmental conditions 25 , 29 , 30 . A new steady-state soil C content will be reached after a few decades when rates of decomposition in the soil at the higher C content match the increased rates of C inputs.

Overseas land conversion

We estimate that the land area needed to make up for shortfalls in domestic production is nearly five times the current overseas land area used for food for England and Wales (Fig.  4 ). Total agricultural land-use is therefore 1.5 times greater than the conventional baseline (combining domestic and overseas land). This is considerably greater than the 16–33% increase in land requirements projected in a recent study of global conversion to organic farming 31 . The difference reflects the high conventional crop yields and livestock productivity in the UK compared with countries using less intensive, lower-yielding farming, and the correspondingly greater production penalties in conversion to organic methods 32 .

Overseas land area needed for imported food. The area required to offset shortfalls in domestic production under organic methods is over five times that under conventional methods, largely due to imports of oilseeds, pork, poultry meat, eggs and milk. Note only the products listed in Fig.  1 are included; products that are not produced in the UK on a large scale (such as maize, rice, tea, coffee and sugar cane) are excluded (Methods). Source Data are provided as a Source Data file

The consequences for net GHG emissions will depend on the nature of the land use change. If it entails conversion of existing natural or semi-natural vegetation or pasture to crops, the cost will be greater than for increased production from existing arable land, which will have already lost C compared with its original natural state, and which might be expected to sequester some C from the atmosphere under organic management. The emissions associated with land use changes will apply over a similar period to the potential gains from enhanced soil C sequestration (i.e., a few decades). We compare three ways of assessing this and associated soil C sequestration: first, if all the additional production is on land formerly under grass, with no associated C sequestration; second, if half the additional production is on land formerly under grass, with a low rate of C sequestration; and third, if a quarter of the additional production is on land formerly under grass, with a high rate of C sequestration (Methods).

In addition, there is the opportunity cost of the amount of C that could be sequestered if the land were instead used to maximise its C storage potential, for example by converting it to productive forest. This aspect is considered by Searchinger et al. 35 , who define a ‘Carbon Opportunity Cost’ (COC) as the amount of C that could be sequestered annually per kg of agricultural commodity if the land were instead used to regenerate forest. We also calculated this (Methods).

The results (Fig.  3b, d and Table 1 ) show that the net effects are sensitive to both the LUC scenario and the degree of soil C sequestration. If all the LUC is by conversion of grassland with no C sequestration (the High scenario), net emissions increase by 56% over the conventional baseline. Whereas, if only 25% of the LUC is from grassland, with a high rate of C sequestration (the Low scenario), net emissions are comparable to those in the conventional baseline. With 50% LUC from grassland, and a moderate rate of C sequestration (the Medium scenario), the net increase is 21%. However, if the COC is added in, the net GHG costs of organic production are much worse. For the Medium LUC and C sequestration scenario, adding in the COC (35.7 ± 6.6 Mt CO 2 e yr −1 ) gives a net increase in emissions over the conventional baseline of 1.7 times.

The results show that widespread adoption of organic farming practices would lead to net increases in GHG emissions as a result of lower crop and livestock yields and hence the need for additional production and associated land use changes overseas. It is not obvious how additional overseas land could be found, without expanding the existing area of tilled land by ploughing up grassland. The global demand for food is expected to increase by 59–98% by 2050 34 . Given that land resources are finite, this implies more competition for land, and more-intensive food production per unit land area, whereas current organic systems are inherently less intensive.

There are undoubted local environmental benefits to organic farming practices, including soil C storage, reduced exposure to pesticides and improved biodiversity. However, these potential benefits need to be set against the requirement for greater production elsewhere. As well as increased GHG emissions from compensatory changes in land use to make up for production shortfalls, there are substantial opportunity costs from reduced availability of land for other purposes, such as greater C storage under natural vegetation 35 . Further, although organic systems may favour increased local biodiversity, habitat fragmentation under low-yielding organic systems may mean global species diversity is in fact greater under land-sparing, high-yielding systems 36 , 37 .

Could yields under organic management be improved to reduce land requirements? Improvements in organic rotation design and more effective and reliable supplies of N from biological fixation are possibilities 38 , 39 . However, these improvements are probably marginal, given the fundamental requirement for more leys in rotations under organic management. Given the much larger contribution of livestock farming to GHG emissions, a greater impact could be gained from reduced meat consumption. Less livestock farming could release land for crops for human consumption and for other purposes such as C storage 40 . However, against this, global trends are towards greater per capita and total meat consumption 33 . Also livestock can play important roles in local nutrient cycling and the provision of ecosystem services 41 , 42 .

In summary, our assessment of the impacts of a 100% conversion to organic farming in England and Wales has revealed that, whilst improvements in resource use efficiency could be obtained, reduced outputs would mean that more imports would be required to maintain food supplies. This major expansion in agricultural cultivation overseas to make up for domestic supply shortfalls would lead to increased GHG emissions from the associated land use changes. Ultimately it is unlikely that there exists any single optimal approach to achieving environmentally sustainable food production. Therefore, context-specific evaluations are required to reveal the extent to which organic systems can contribute, alongside other approaches, to multi-objective and internationally binding sustainability targets.

The OLUM (Optimal Land Use Model) 16 is a linear programming (LP) model that includes a suite of organic farming activities that take place in nine Robust Farm Types: specialist cropping, mixed arable and livestock, specialist dairy, lowland grazing livestock, Less Favoured Area (LFA) grazing livestock, pigs and poultry, and other. These cover the entire agricultural land-base in England and Wales. The Objective Function of the model, which is maximised subject to constraints on resource availabilities, is the sum of total crop and livestock production, expressed as ME. Although human diets also need proteins, fats and nutrients, energy requirements are deemed to be a primary driver of consumption and an inadequate food-energy intake is almost always accompanied by insufficient intake of nutrients 37 .

The basic formulation of the OLUM is

where \(Z\) is the objective function to be maximised, C ij is the ME output (fresh weight per unit crop area or livestock number yr −1 ) of agricultural product i on soil × rain class j , x ij is a scalar for the agricultural activity (crop area or livestock number), Rx ij is a factor for the input and resource requirement associated with the agricultural activity, and b is a vector for resource endowment and input availability (e.g., land by soil and rainfall class, and available soil N). Human dietary change is not considered.

In each farm type, the set of crop and livestock production activities available are fixed, as evidence suggests that the dominant agricultural activity (e.g., dairy farming) will usually stay in place post conversion to organic management, due to existing farm infrastructure, farming knowledge and local conditions 43 . However, these activities can be individually expanded and contracted endogenously. The land areas under each farm type are fixed, reflecting the areal coverage of their conventional equivalents recorded in the June Survey of Agriculture in 2010 44 . A number of logical constraints are applied in the model to reflect: the availability of land in the various soil/rainfall classes (next paragraph); maximum permissible area of crop groups (e.g., cereals, root crops) reflecting rotational constraints; and upper limits on the total output of each crop, set at 150% of the current supply, following an assumption that further increases could not be absorbed by the market. Rotational N availability limits are also imposed, as determined by crop and livestock-product offtake (from the land), N supply from various sources, such as biological fixation, imported feed and atmospheric deposition, as well as manure-N availability in each region. We assume balances of P and K are maintained by applying P and K minerals commonly used in organic systems. Livestock numbers and associated product output volumes are constrained by feed availability, as well as maximum and minimum stocking density constraints.

Heavy, medium, light and humose soil classes are defined with specified organic matter contents and pH values, and their spatial distribution across England and Wales in 5 km × 5 km grid squares were obtained from the National Soil Inventory ( www.LandIS.org.uk ). Four rainfall classes are defined based on 30-year Meteorological Office annual rainfall data: dry 539–635 mm, medium 636–723 mm, wet 724–823 mm and very wet 824–2500 mm. The total areas of each soil × rainfall combination were determined by identifying the dominant combination in each 5 km × 5 km grid square and allocating to that combination the sum of the areas of each square, less any non-agricultural area.

The OLUM produces a best estimate of production under fully organic agriculture in England and Wales, assuming that food production would be maximised. To ensure that the results are reasonable, outputs are compared to the real-world distribution of conventional production in 2010 derived from a range of industry sources (Supplementary Table  4 ), and to results from a previous study on the production impacts of a switch to organic farming in England and Wales 15 .

The Agri-LCA models

We assessed the environmental impacts of conversion to organic farming using the Cranfield Agri-LCA models for England and Wales 3 . Fossil energy use and emissions of CO 2 , CH 4 and N 2 O per tonne of each food commodity produced under given soil and management conditions are combined with official data on levels of production, to provide estimates of the total GHG impact of agriculture. Results from earlier emissions analyses generated by these models for the current mix of agricultural systems in England and Wales are used as a comparator against which to assess the organic conversion scenario. We adjust the following components of the Agri-LCA models to better reflect organic agriculture using data sources listed in Supplementary Table  4 : first, crop and grassland yields; second, crop cultivation practices and manure/compost application rates; third, crop and grassland areas by soil and rainfall type; fourth, livestock productivity and mortality rates; and fifth, livestock diet compositions.

Crop yield, cultivation and manure application data are adjusted for 12 main crops: wheat, barley, rye, oats, potatoes, oilseed rape, sugar beet, beans and peas, cabbage, carrots, onions and forage maize. These cover 98% of the cultivated land in England and Wales 44 . All data sources used in this exercise are provided in Supplementary Table  4 . Crop and grassland areas under each of 16 soil and rainfall classes are derived from the OLUM results. The crop areas, by each soil and rainfall class, are used in the Agri-LCA models to adjust N 2 O and CO 2 impacts to reflect organic management. The functional units used in the LCA are tonnes of marketed crop-product.

Organic animal production data for the Agri-LCA are drawn from a range of industry sources to define, by livestock type: daily live-weight gain, annual fat-corrected milk yield, and feed conversion ratios. Data are also input to the Agri-LCA on the composition of livestock diets, stocking rates per hectare and the proportion of livestock on upland and lowland. These values ensure that feed intake meets the ME demand of livestock. Nitrogen excretion from livestock is derived from mass balances. Compound feed composition data are also applied to determine embedded impacts of feed production overseas. Direct CH 4 emissions from livestock are calculated as a function of dry matter intake (scaled in proportion to the forage dry matter intake), live-weight and milk yields. The Agri-LCA livestock emissions estimates are based on six commodities: eggs, milk, sheep, beef, pig and poultry meat. Meat outputs are defined in terms of total dressed carcass weight (tonnes), eggs by weight (tonnes) and milk output as fat-corrected litres 3 .

System boundaries and allocation of environmental burdens

The downstream system boundary applied in the Agri-LCA modelling is the farm gate, i.e., only resources consumed during the production of inputs and on-farm-based processes are considered (i.e., ‘from cradle to farm gate’ 2 ). The GHG emissions associated with downstream activities—such as distribution, consumption and disposal of products produced on the farm—are not included. Some on-farm processing, such as grain drying, milk cooling and potato storage, are included in the total impact assessment, as these operations are considered to be part of the on-farm production process 3 . Where multiple products are derived from the same agricultural activity, such as grain and straw from cereals production, the GHG emissions from fossil energy use associated with the different components are allocated on the basis of relative economic value and by system expansion with regard to manure (i.e., the manufactured N fertiliser avoided is discounted from the environmental burdens associated with non-organic crops). Where economic allocation is used in the Agri-LCA, an organic price differential is applied. Emission factors are derived from IPCC 2006 estimates and total emissions of CH 4 and N 2 O converted to CO 2 equivalents using their 100-year Global Warming Potentials (GWPs). The time-dependency of the GWP values introduces some uncertainty, particularly for CH 4 which has a 20-year GWP more than twice its 100-year value. However, allowing for this would introduce undue complexity. The emissions associated with animal feed production are allocated to the livestock emission estimates, not those for crop production.

Imports and exports

The GHG emissions associated with producing imported food are allowed for in the Agri-LCA models. We assume that any shortfall in supply from organic agriculture is made up by increased imports of organically produced commodities from overseas. We use data from industry sources (Supplementary Table  5 ) to allocate imported product to the historic regions of origin of imports 45 . The GHG emissions associated with the transport of imports to England and Wales is determined by multiplying the total volume of imported products by GHG coefficients derived from Hess et al. 45 . Transport burdens for imported sugar and sheep meat are derived from Plassman et al. 46 and Webb et al. 23 , respectively.

Where the OLUM generates crop and livestock production in excess of domestic demand, the surpluses are assumed to be exported and the GHG and fossil energy burdens associated with production of the exported commodities are subtracted from the total environmental burdens of organic agriculture. The same adjustment is made to the GHG estimates of exports for conventional agriculture (see data sources for export volumes in Supplementary Table  5 ). Where the OLUM reduces production below the level of domestic demand it is assumed that no exports occur, i.e., domestic consumption would take priority.

Fossil energy use and GHG emissions associated with the production of oilseed rape, sugar beet, wheat and lamb from non-European countries are derived from Pelletier et al. 47 , Tzilivakis et al. 48 and Webb et al. 23 . The environmental burdens associated with crop and livestock products sourced from Scotland, Northern Ireland and the rest of Europe are derived from the Agri-LCA, under the assumption that similar emissions and fossil energy use would occur in these systems 3 .

We obtain an upper estimate of potential sequestration rates in organic systems based on rates of change of soil C measured in the National Soil Inventory (NSI) of England and Wales 49 , as follows. Kirk and Bellamy 28 summarised the NSI results by fitting to the data the simple single-pool model

where C is the C content per unit land surface area, I is the rate of input from vegetation and other sources and k is a rate constant for decomposition. They fitted Eq. ( 2 ) to the data for each NSI land use class separately, omitting organic soils (which accounted for <5% of all the soils in the NSI) because their rates of change were less certain. The soil C content at steady state, when dC/dt  = 0, is equal to I/k . Soils with C contents greater than the steady-state value lose C; those with C contents less than it sequester C.

We take the NSI class ‘rotational grass’ (i.e., grass that is sown and then tilled every few years as part of an arable rotation) to represent potential C contents under ideal organic management, and the class ‘arable’ to represent C contents under conventional arable management. The mean soil C contents were 43.2 ( n  = 552 sites) and 58.7 ( n  = 301 sites) Mg C ha − 1 under arable and rotational grass, respectively, and the calculated steady-state C contents were 37.6 and 55.0 Mg C ha −1 , respectively, indicating the rotational grass soils were on average close to steady state and their C contents therefore represent maximum potential sequestration levels. The values of I and k for rotational grass were 2.54 Mg C ha −1  yr −1 and 0.046 yr −1 , respectively (equivalent to negative emissions of −9.3 and −0.17 Mg CO 2  ha −1  yr −1 ). Substituting these values and the mean arable C content in Eq. ( 2 ) gives for the mean rate of sequestration on conversion from arable to rotational grass ((2.54 − 0.046 × 43.2) + 0)/2 = 0.28 Mg C ha −1  yr −1 (or −1.03 Mg CO 2  ha −1  yr −1 ). After adjusting for the proportion of arable to arable plus rotational grass, the rate is 0.28 × 552/(552 + 301)=0.18 Mg C ha −1  yr −1 (or 0.66 Mg CO 2  ha −1  yr −1 ). We use this as the high C sequestration rate in Fig.  3 . We assume sequestration rates in established swards of permanent pasture or rough grazing to be zero given that these sites will have already reached steady state.

For comparison, in a literature survey of experiments comparing conventional and organic farming, Gattinger et al. 24 found sequestration rates between 0.07 and 0.45 Mg C ha −1 . However, most of these comparisons involved very high rates of external organic matter inputs to the organic systems. The average inputs were four times those under conventional farming for the full dataset and two times for systems with inputs equivalent to those from one European Livestock Unit (ELU) ha −1 26 . We calculate that quantities of farmyard manure would be only approximately 12% greater under organic farming, as a result of greater numbers of grazing livestock (Supplementary Table  3 ). We therefore consider Gattinger et al.’s upper and middle sequestration estimates to be unrepresentative and take as the moderate sequestration rate in Fig.  3 their lower value of 0.07 Mg C ha −1  yr −1 .

Gains through C sequestration will be time-limited, because any given soil has a finite capacity to accumulate C and a new steady-state C content will be reached after a few years, when increased C inputs are matched by increased losses at the greater soil C content. Our estimated sequestration rates therefore only apply in the early-years following conversion to organic methods. Based on the NSI data, a new steady-state C content on conversion from arable to rotational grass would only be attained after (55.02 − 43.15)/0.28 = 42 years.

Additional emissions from overseas LUC and C sequestration

We estimate the additional overseas land area required for each of the food products listed in Fig.  1 , produced organically, as follows. For crops, we use first, regional yield data from Eurostat, second, organic crop yields from the recent meta-analysis by de Ponti et al. 32 and third, results of an LCA for milling wheat grown in Canada 47 . For livestock, we use first, regional yield data from Eurostat, second, results from the Agri-LCA 3 and third, recent studies on the environmental burdens of imported lamb from New Zealand 23 , 50 . The additional land area is calculated from the total overseas area required less the amount required for imports in the conventional baseline (based on the values in Supplementary Table  6 ). The corresponding emissions are calculated as follows.

We assume that woodland would not be converted for food production as this would conflict with the principles of the International Federation of Organic Agriculture Movements (IFOAM) 51 . We calculate emissions from the conversion of grassland to crops from the area converted multiplied by LUC emission estimates specified by the British Standards Institute for a range of countries 52 . Considering that not all the LUC would be from grassland, we compare three ways of assessing the net emissions from overseas LUC and associated soil C sequestration, plus that of home production, as follows. First, High: all the additional land required is converted from grassland, with no net soil C sequestration at home or overseas. Second, Medium: 50% of the additional arable land is converted from grassland, with a moderate rate of C sequestration (0.07 Mg C ha −1  yr −1 ) at home and overseas. Third, Low: 25% of the additional arable land is converted from grassland, with a high rate of C sequestration (0.18 Mg C ha −1  yr −1 ) at home and overseas.

Following Searchinger et al. 35 , we also calculate the additional ‘carbon opportunity cost’ (COC) of using the land for agriculture as the quantity of C that could be sequestered annually if the average productive capacity of land used to produce 1 kg of each food product globally were instead devoted to regenerating forest. We calculate the total COC from Searchinger et al.’s 35 COC factors per unit fresh weight of each food product (separating crops for human consumption from those used as animal feeds) multiplied by the additional fresh weight imports of each product required to offset home production shortfalls. This is in addition to the emissions calculated under the LUC and C sequestration scenarios (1)–(3) above. This ‘C gain’ method—as opposed to a ‘C loss’ method based on plant and soil C lost to date per unit food production—applies if it is only possible to increase C by re-establishing forests.

Uncertainty analysis

Estimates of uncertainty for each main commodity analysed were produced following the method of Wiltshire et al. 53 . Uncertainties were derived using Monte Carlo simulations with each domestically produced crop commodity given an uncertainty estimate of 10% (i.e., in a triangular distribution with upper and lower bounds at 10% of the mean) and each domestically produced livestock commodity at 15%. The emissions for crops and livestock were summed in separate Monte Carlo simulations to produce overall uncertainty estimates for each sector (as the standard deviation). These were increased by 15% for all imported commodities en bloc. Emissions from import transportation were assumed to have a standard deviation of 10% of the mean 53 , i.e., the coefficient of variation (CV) is 10%. The areas of land derived by the LP were assumed to have an error of 15%, which was applied to the whole solution, not per crop, given that all areas were derived from any individual solution. Error bars on production area per crop (or livestock commodity) are thus not shown.

The final emissions and uncertainty estimates for each production system were derived from the sum of emissions from domestically produced crops and livestock together with emissions from imported crop and livestock production, together with their transport emissions, based on supply chain data from Webb et al. (2013) 23 and Williams et al. (2017) 54 . Estimates of the uncertainty from LUC were derived from Houghton 55 and those from C sequestration from Kirk and Bellamy 28 for the upper rate and from Gattinger et al. 24 for the medium and lowest rates. These were implemented as the uncertainty being a proportion of the means that were applied to the LUC and C sequestration scenarios. These were established as having a CV of 17% for LUC 55 , which was increased for the carbon opportunity cost of Searchinger et al. 35 by a factor of 1.5 to allow for the extra uncertainty of the method (i.e., CV of 26%). The uncertainty of the high level of C sequestration was 86 and 24% for lower levels.

The uncertainty estimates for the sum of crop and livestock commodities, transport and land use change emissions and sequestration are summarised in Supplementary Table  7 . These were used as input values of uncertainties in the last stage to derive the overall uncertainties of each scenario. We tested the significance of differences in mean values, z , using Eq. ( 3 ) 53

where m A and m B are the means of systems A and B, respectively, and CV is the CV of each mean (expressed as whole numbers). The threshold for a significant difference at the 5% level was z  ≥ 1.96.

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this article.

Data availability

The data underlying these calculations can be accessed at: https://doi.org/10.6084/m9.figshare.6080333.v2 . OLUM model code and data can be accessed at: https://tinyurl.com/yxlszsrv . The Agri-LCA models and data can be accessed at: https://tinyurl.com/yy5jol7c

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Acknowledgements

We thank the Soil Association, Vitrition Organic Feeds and AHDB for providing data for the land-use model. L.S. was supported by the Organic Research Centre, a PhD studentship from the Engineering and Physical Sciences Research Council (EPSRC grant ref. WG17023N) and an education grant awarded by the Ratcliff Foundation. We thank Dr. Bruce Pearce at the Organic Research Centre for his comments on an early draft of the paper.

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Laurence G. Smith, Guy J. D. Kirk & Adrian G. Williams

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L.S., P.J., A.W. and G.K. designed and developed the OLUM and LS carried the model simulations. A.W. developed the Agri-LCA models, provided an overview of their function for use in this study and access to relevant data for the calculation of the environmental impacts. G.K. provided guidance on the calculation of the greenhouse gas emission offset from carbon sequestration in organic systems. L.S. and G.K. wrote and revised the paper with help from all co-authors.

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Smith, L.G., Kirk, G.J.D., Jones, P.J. et al. The greenhouse gas impacts of converting food production in England and Wales to organic methods. Nat Commun 10 , 4641 (2019). https://doi.org/10.1038/s41467-019-12622-7

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Article Contents

Introduction, organic farming process, benefits of organic farming, organic agriculture and sustainable development, status of organic farming in india: production, popularity, and economic growth, future prospects of organic farming in india, conclusions, conflict of interest.

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Organic farming in India: a vision towards a healthy nation

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Suryatapa Das, Annalakshmi Chatterjee, Tapan Kumar Pal, Organic farming in India: a vision towards a healthy nation, Food Quality and Safety , Volume 4, Issue 2, May 2020, Pages 69–76, https://doi.org/10.1093/fqsafe/fyaa018

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Food quality and safety are the two important factors that have gained ever-increasing attention in general consumers. Conventionally grown foods have immense adverse health effects due to the presence of higher pesticide residue, more nitrate, heavy metals, hormones, antibiotic residue, and also genetically modified organisms. Moreover, conventionally grown foods are less nutritious and contain lesser amounts of protective antioxidants. In the quest for safer food, the demand for organically grown foods has increased during the last decades due to their probable health benefits and food safety concerns. Organic food production is defined as cultivation without the application of chemical fertilizers and synthetic pesticides or genetically modified organisms, growth hormones, and antibiotics. The popularity of organically grown foods is increasing day by day owing to their nutritional and health benefits. Organic farming also protects the environment and has a greater socio-economic impact on a nation. India is a country that is bestowed with indigenous skills and potentiality for growth in organic agriculture. Although India was far behind in the adoption of organic farming due to several reasons, presently it has achieved rapid growth in organic agriculture and now becomes one of the largest organic producers in the world. Therefore, organic farming has a great impact on the health of a nation like India by ensuring sustainable development.

Food quality and safety are two vital factors that have attained constant attention in common people. Growing environmental awareness and several food hazards (e.g. dioxins, bovine spongiform encephalopathy, and bacterial contamination) have substantially decreased the consumer’s trust towards food quality in the last decades. Intensive conventional farming can add contamination to the food chain. For these reasons, consumers are quested for safer and better foods that are produced through more ecologically and authentically by local systems. Organically grown food and food products are believed to meet these demands ( Rembialkowska, 2007 ).

In recent years, organic farming as a cultivation process is gaining increasing popularity ( Dangour et al. , 2010 ). Organically grown foods have become one of the best choices for both consumers and farmers. Organically grown foods are part of go green lifestyle. But the question is that what is meant by organic farming? ( Chopra et al. , 2013 ).

The term ‘organic’ was first coined by Northbourne, in 1940, in his book entitled ‘Look to the Land’.

Northbourne stated that ‘the farm itself should have biological completeness; it must be a living entity; it must be a unit which has within itself a balanced organic life’( Nourthbourne, 2003 ). Northbourne also defined organic farming as ‘an ecological production management system that promotes and enhances biodiversity, biological cycles and soil biological activity’. According to Winter and Davis (2006) , ‘it is based on minimal use of off-farm inputs and on management practices that restore, maintain and enhance ecological harmony’.

They mentioned that organic produce is not grown with synthetic pesticides, antibiotics, growth hormones, application of genetic modification techniques (such as genetically modified crops), sewage sludge, or chemical fertilizers.

Whereas, conventional farming is the cultivation process where synthetic pesticide and chemical fertilizers are applied to gain higher crop yield and profit. In conventional farming, synthetic pesticides and chemicals are able to eliminate insects, weeds, and pests and growth factors such as synthetic hormones and fertilizers increase growth rate ( Worthington, 2001 ).

As synthetically produced pesticides and chemical fertilizers are utilized in conventional farming, consumption of conventionally grown foods is discouraged, and for these reasons, the popularity of organic farming is increasing gradually.

Organic farming and food processing practices are wide-ranging and necessitate the development of socially, ecologically, and economically sustainable food production system. The International Federation of Organic Agriculture Movements (IFOAM) has suggested the basic four principles of organic farming, i.e. the principle of health, ecology, fairness, and care ( Figure 1 ). The main principles and practices of organic food production are to inspire and enhance biological cycles in the farming system, keep and enhance deep-rooted soil fertility, reduce all types of pollution, evade the application of pesticides and synthetic fertilizers, conserve genetic diversity in food, consider the vast socio-ecological impact of food production, and produce high-quality food in sufficient quantity ( IFOAM, 1998 ).

Principles of organic farming (adapted from IFOAM, 1998 ).

According to the National Organic Programme implemented by USDA Organic Food Production Act (OFPA, 1990), agriculture needs specific prerequisites for both crop cultivation and animal husbandry. To be acceptable as organic, crops should be cultivated in lands without any synthetic pesticides, chemical fertilizers, and herbicides for 3 years before harvesting with enough buffer zone to lower contamination from the adjacent farms. Genetically engineered products, sewage sludge, and ionizing radiation are strictly prohibited. Fertility and nutrient content of soil are managed primarily by farming practices, with crop rotation, and using cover crops that are boosted with animal and plant waste manures. Pests, diseases, and weeds are mainly controlled with the adaptation of physical and biological control systems without using herbicides and synthetic pesticides. Organic livestock should be reared devoid of scheduled application of growth hormones or antibiotics and they should be provided with enough access to the outdoor. Preventive health practices such as routine vaccination, vitamins and minerals supplementation are also needed (OFPA, 1990).

Nutritional benefits and health safety

Magnusson et al. (2003) and Brandt and MØlgaord (2001) mentioned that the growing demand for organically farmed fresh products has created an interest in both consumer and producer regarding the nutritional value of organically and conventionally grown foods. According to a study conducted by AFSSA (2003) , organically grown foods, especially leafy vegetables and tubers, have higher dry matter as compared to conventionally grown foods. Woëse et al. (1997) and Bourn and Prescott (2002) also found similar results. Although organic cereals and their products contain lesser protein than conventional cereals, they have higher quality proteins with better amino acid scores. Lysine content in organic wheat has been reported to be 25%–30% more than conventional wheat ( Woëse et al. , 1997 ; Brandt et al. , 2000 ).

Organically grazed cows and sheep contain less fat and more lean meat as compared to conventional counterparts ( Hansson et al. , 2000 ). In a study conducted by Nürnberg et al. (2002) , organically fed cow’s muscle contains fourfold more linolenic acid, which is a recommended cardio-protective ω-3 fatty acid, with accompanying decrease in oleic acid and linoleic acid. Pastushenko et al. (2000) found that meat from an organically grazed cow contains high amounts of polyunsaturated fatty acids. The milk produced from the organic farm contains higher polyunsaturated fatty acids and vitamin E ( Lund, 1991 ). Vitamin E and carotenoids are found in a nutritionally desirable amount in organic milk ( Nürnberg et al. , 2002 ). Higher oleic acid has been found in organic virgin olive oil ( Gutierrez et al. , 1999 ). Organic plants contain significantly more magnesium, iron, and phosphorous. They also contain more calcium, sodium, and potassium as major elements and manganese, iodine, chromium, molybdenum, selenium, boron, copper, vanadium, and zinc as trace elements ( Rembialkowska, 2007 ).

According to a review of Lairon (2010) which was based on the French Agency for food safety (AFSSA) report, organic products contain more dry matter, minerals, and antioxidants such as polyphenols and salicylic acid. Organic foods (94%–100%) contain no pesticide residues in comparison to conventionally grown foods.

Fruits and vegetables contain a wide variety of phytochemicals such as polyphenols, resveratrol, and pro-vitamin C and carotenoids which are generally secondary metabolites of plants. In a study of Lairon (2010) , organic fruits and vegetables contain 27% more vitamin C than conventional fruits and vegetables. These secondary metabolites have substantial regulatory effects at cellular levels and hence found to be protective against certain diseases such as cancers, chronic inflammations, and other diseases ( Lairon, 2010 ).

According to a Food Marketing Institute (2008) , some organic foods such as corn, strawberries, and marionberries have greater than 30% of cancer-fighting antioxidants. The phenols and polyphenolic antioxidants are in higher level in organic fruits and vegetables. It has been estimated that organic plants contain double the amount of phenolic compounds than conventional ones ( Rembialkowska, 2007 ). Organic wine has been reported to contain a higher level of resveratrol ( Levite et al. , 2000 ).

Rossi et al. (2008) stated that organically grown tomatoes contain more salicylic acid than conventional counterparts. Salicylic acid is a naturally occurring phytochemical having anti-inflammatory and anti-stress effects and prevents hardening of arteries and bowel cancer ( Rembialkowska, 2007 ; Butler et al. , 2008 ).

Total sugar content is more in organic fruits because of which they taste better to consumers. Bread made from organically grown grain was found to have better flavour and also had better crumb elasticity ( BjØrn and Fruekidle, 2003 ). Organically grown fruits and vegetables have been proved to taste better and smell good ( Rembialkowska, 2000 ).

Organic vegetables normally have far less nitrate content than conventional vegetables ( Woëse et al. , 1997 ). Nitrates are used in farming as soil fertilizer but they can be easily transformed into nitrites, a matter of public health concern. Nitrites are highly reactive nitrogen species that are capable of competing with oxygen in the blood to bind with haemoglobin, thus leading to methemoglobinemia. It also binds to the secondary amine to generate nitrosamine which is a potent carcinogen ( Lairon, 2010 ).

As organically grown foods are cultivated without the use of pesticides and sewage sludge, they are less contaminated with pesticide residue and pathogenic organisms such as Listeria monocytogenes or Salmonella sp. or Escherichia coli ( Van Renterghem et al. , 1991 ; Lung et al. , 2001 ; Warnick et al. , 2001 ).

Therefore, organic foods ensure better nutritional benefits and health safety.

Environmental impact

Organic farming has a protective role in environmental conservation. The effect of organic and conventional agriculture on the environment has been extensively studied. It is believed that organic farming is less harmful to the environment as it does not allow synthetic pesticides, most of which are potentially harmful to water, soil, and local terrestrial and aquatic wildlife ( Oquist et al. , 2007 ). In addition, organic farms are better than conventional farms at sustaining biodiversity, due to practices of crop rotation. Organic farming improves physico-biological properties of soil consisting of more organic matter, biomass, higher enzyme, better soil stability, enhanced water percolation, holding capacities, lesser water, and wind erosion compared to conventionally farming soil ( Fliessbach & Mäder, 2000 ; Edwards, 2007 ; Fileβbach et al. , 2007 ). Organic farming uses lesser energy and produces less waste per unit area or per unit yield ( Stolze et al. , 2000 ; Hansen et al. , 2001 ). In addition, organically managed soils are of greater quality and water retention capacity, resulting in higher yield in organic farms even during the drought years ( Pimentel et al. , 2005 ).

Socioeconomic impact

Organic cultivation requires a higher level of labour, hence produces more income-generating jobs per farm ( Halberg, 2008 ). According to Winter and Davis (2006), an organic product typically costs 10%–40% more than the similar conventionally crops and it depends on multiple factors both in the input and the output arms. On the input side, factors that enhance the price of organic foods include the high cost of obtaining the organic certification, the high cost of manpower in the field, lack of subsidies on organics in India, unlike chemical inputs. But consumers are willing to pay a high price as there is increasing health awareness. Some organic products also have short supply against high demand with a resultant increase in cost ( Mukherjee et al. , 2018 ).

Biofertilizers and pesticides can be produced locally, so yearly inputs invested by the farmers are also low ( Lobley et al. , 2005 ). As the labours working in organic farms are less likely to be exposed to agricultural chemicals, their occupational health is improved ( Thompson and Kidwell, 1998 ). Organic food has a longer shelf life than conventional foods due to lesser nitrates and greater antioxidants. Nitrates hasten food spoilage, whereas antioxidants help to enhance the shelf life of foods ( Shreck et al. , 2006 ). Organic farming is now an expanding economic sector as a result of the profit incurred by organic produce and thereby leading to a growing inclination towards organic agriculture by the farmers.

The concept of sustainable agriculture integrates three main goals—environmental health, economic profitability, and social and economic equity. The concept of sustainability rests on the principle that we must meet the needs of the present without compromising the ability of future generations to meet their own needs.

The very basic approach to organic farming for the sustainable environment includes the following ( Yadav, 2017 ):

Improvement and maintenance of the natural landscape and agro-ecosystem.

Avoidance of overexploitation and pollution of natural resources.

Minimization of the consumption of non-renewable energy resources.

Exploitation synergies that exist in a natural ecosystem.

Maintenance and improve soil health by stimulating activity or soil organic manures and avoid harming them with pesticides.

Optimum economic returns, with a safe, secure, and healthy working environment.

Acknowledgement of the virtues of indigenous know-how and traditional farming system.

Long-term economic viability can only be possible by organic farming and because of its premium price in the market, organic farming is more profitable. The increase in the cost of production by the use of pesticides and fertilizers in conventional farming and its negative impact on farmer’s health affect economic balance in a community and benefits only go to the manufacturer of these pesticides. Continuous degradation of soil fertility by chemical fertilizers leads to production loss and hence increases the cost of production which makes the farming economically unsustainable. Implementation of a strategy encompassing food security, generation of rural employment, poverty alleviation, conservation of the natural resource, adoption of an export-oriented production system, sound infrastructure, active participation of government, and private-public sector will be helpful to make revamp economic sustainability in agriculture ( Soumya, 2015 ).

Social sustainability

It is defined as a process or framework that promotes the wellbeing of members of an organization while supporting the ability of future generations to maintain a healthy community. Social sustainability can be improved by enabling rural poor to get benefit from agricultural development, giving respect to indigenous knowledge and practices along with modern technologies, promoting gender equality in labour, full participation of vibrant rural communities to enhance their confidence and mental health, and thus decreasing suicidal rates among the farmers. Organic farming appears to generate 30% more employment in rural areas and labour achieves higher returns per unit of labour input ( Pandey and Singh, 2012 ).

Organic food and farming have continued to grow across the world. Since 1985, the total area of farmland under organic production has been increased steadily over the last three decades ( Willer and Lernoud, 2019 ). By 2017, there was a total of 69.8 million hectares of organically managed land recorded globally which represents a 20% growth or 11.7 million hectares of land in comparison to the year 2016. This is the largest growth ever recorded in organic farming ( Willer and Lernoud, 2019 ). The countries with the largest areas of organic agricultural land recorded in the year 2017 are given in Figure 2 . Australia has the largest organic lands with an area of 35.65 million hectares and India acquired the eighth position with a total organic agriculture area of 1.78 million hectares ( Willer and Lernoud, 2019 ).

Country-wise areas of organic agriculture land, 2017 ( Willer and Lernoud, 2019 ).

In 2017, it was also reported that day to day the number of organic produces increases considerably all over the world. Asia contributes to the largest percentage (40%) of organic production in the world and India contributes to be largest number of organic producer (835 000) ( Figures 3 and 4 ).

Organic producers by region, 2017 ( Willer and Lernoud, 2019 ).

Largest organic producers in the world, 2017 ( Willer and Lernoud, 2017 ).

The growth of organic farming in India was quite dawdling with only 41 000 hectares of organic land comprising merely 0.03% of the total cultivated area. In India during 2002, the production of organic farming was about 14 000 tonnes of which 85% of it was exported ( Chopra et al. , 2013 ). The most important barrier considered in the progress of organic agriculture in India was the lacunae in the government policies of making a firm decision to promote organic agriculture. Moreover, there were several major drawbacks in the growth of organic farming in India which include lack of awareness, lack of good marketing policies, shortage of biomass, inadequate farming infrastructure, high input cost of farming, inappropriate marketing of organic input, inefficient agricultural policies, lack of financial support, incapability of meeting export demand, lack of quality manure, and low yield ( Figure 5 ; Bhardwaj and Dhiman, 2019 ).

Constraints of organic farming in India in the past ( Bhardwaj and Dhiman, 2019 ).

Recently, the Government of India has implemented a number of programs and schemes for boosting organic farming in the country. Among these the most important include (1) The Paramparagat Krishi Vikas Yojana, (2) Organic Value Chain Development in North Eastern Region Scheme, (3) Rashtriya Krishi Vikas Yojana, (4) The mission for Integrated Development of Horticulture (a. National Horticulture Mission, b. Horticulture Mission for North East and Himalayan states, c. National Bamboo Mission, d. National Horticulture Board, e. Coconut Development Board, d. Central Institute for Horticulture, Nagaland), (5) National Programme for Organic Production, (6) National Project on Organic Farming, and (7) National Mission for Sustainable Agriculture ( Yadav, 2017 ).

Zero Budget Natural Farming (ZBNF) is a method of farming where the cost of growing and harvesting plants is zero as it reduces costs through eliminating external inputs and using local resources to rejuvenate soils and restore ecosystem health through diverse, multi-layered cropping systems. It requires only 10% of water and 10% electricity less than chemical and organic farming. The micro-organisms of Cow dung (300–500 crores of beneficial micro-organisms per one gram cow dung) decompose the dried biomass on the soil and convert it into ready-to-use nutrients for plants. Paramparagat Krishi Vikas Yojana since 2015–16 and Rashtriya Krishi Vikas Yojana are the schemes taken by the Government of India under the ZBNF policy ( Sobhana et al. , 2019 ). According to Kumar (2020) , in the union budget 2020–21, Rs 687.5 crore has been allocated for the organic and natural farming sector which was Rs 461.36 crore in the previous year.

Indian Competence Centre for Organic Agriculture cited that the global market for organically grown foods is USD 26 billion which will be increased to the amount of USD 102 billion by 2020 ( Chopra et al. , 2013 ).

The major states involved in organic agriculture in India are Gujarat, Kerala, Karnataka, Uttarakhand, Sikkim, Rajasthan, Maharashtra, Tamil Nadu, Madhya Pradesh, and Himachal Pradesh ( Chandrashekar, 2010 ).

India ranked 8th with respect to the land of organic agriculture and 88th in the ratio of organic crops to agricultural land as per Agricultural and Processed Food Products Export Development Authority and report of Research Institute of Organic Agriculture ( Chopra et al. , 2013 ; Willer and Lernoud, 2017 ). But a significant growth in the organic sector in India has been observed ( Willer and Lernoud, 2017 ) in the last decades.

There have been about a threefold increase from 528 171 ha in 2007–08 to 1.2 million ha of cultivable land in 2014–15. As per the study conducted by Associated Chambers of Commerce & Industry in India, the organic food turnover is increasing at about 25% annually and thereby will be expected to reach USD 1.36 billion in 2020 from USD 0.36 billion in 2014 ( Willer and Lernoud, 2017 ).

The consumption and popularity of organic foods are increasing day by day throughout the world. In 2008, more than two-thirds of US consumers purchased organic food, and more than one fourth purchased them weekly. The consumption of organic crops has doubled in the USA since 1997. A consumer prefers organic foods in the concept that organic foods have more nutritional values, have lesser or no additive contaminants, and sustainably grown. The families with younger consumers, in general, prefer organic fruits and vegetables than consumers of any other age group ( Thompson et al. , 1998 ; Loureino et al. , 2001 ; Magnusson et al. , 2003 ). The popularity of organic foods is due to its nutritional and health benefits and positive impact on environmental and socioeconomic status ( Chopra et al. , 2013 ) and by a survey conducted by the UN Environment Programme, organic farming methods give small yields (on average 20% lower) as compared to conventional farming ( Gutierrez et al. , 1999 ). As the yields of organically grown foods are low, the costs of them are higher. The higher prices made a barrier for many consumers to buy organic foods ( Lairon, 2010 ). Organic farming needs far more lands to generate the same amount of organic food produce as conventional farming does, as chemical fertilizers are not used here, which conventionally produces higher yield. Organic agriculture hardly contributes to addressing the issue of global climate change. During the last decades, the consumption of organic foods has been increasing gradually, particularly in western countries ( Meiner-Ploeger, 2005 ).

Organic foods have become one of the rapidly growing food markets with revenue increasing by nearly 20% each year since 1990 ( Winter and Davis, 2006 ). The global organic food market has been reached USD 81.6 billion in 2015 from USD 17.9 billion during the year 2000 ( Figure 6 ) and most of which showed double-digit growth rates ( Willer and Lernoud, 2019 ).

Worldwide growth in organic food sales ( Willer and Lernoud, 2019 ).

India is an agriculture-based country with 67% of its population and 55% of manpower depending on farming and related activities. Agriculture fulfils the basic needs of India’s fastest-growing population accounted for 30% of total income. Organic farming has been found to be an indigenous practice of India that practised in countless rural and farming communities over the millennium. The arrival of modern techniques and increased burden of population led to a propensity towards conventional farming that involves the use of synthetic fertilizer, chemical pesticides, application of genetic modification techniques, etc.

Even in developing countries like India, the demand for organically grown produce is more as people are more aware now about the safety and quality of food, and the organic process has a massive influence on soil health, which devoid of chemical pesticides. Organic cultivation has an immense prospect of income generation too ( Bhardwaj and Dhiman, 2019 ). The soil in India is bestowed with various types of naturally available organic nutrient resources that aid in organic farming ( Adolph and Butterworth, 2002 ; Reddy, 2010 ; Deshmukh and Babar, 2015 ).

India is a country with a concrete traditional farming system, ingenious farmers, extensive drylands, and nominal use of chemical fertilizers and pesticides. Moreover, adequate rainfall in north-east hilly regions of the country where few negligible chemicals are employed for a long period of time, come to fruition as naturally organic lands ( Gour, 2016 ).

Indian traditional farmers possess a deep insight based on their knowledge, extensive observation, perseverance and practices for maintaining soil fertility, and pest management which are found effective in strengthening organic production and subsequent economic growth in India. The progress in organic agriculture is quite commendable. Currently, India has become the largest organic producer in the globe ( Willer and Lernoud, 2017 , 2019 ) and ranked eighth having 1.78 million ha of organic agriculture land in the world in 2017 ( Sharma and Goyal, 2000 ; Adolph and Butterworth, 2002 ; Willer and Lernoud, 2019 ).

Various newer technologies have been invented in the field of organic farming such as integration of mycorrhizal fungi and nano-biostimulants (to increase the agricultural productivity in an environmentally friendly manner), mapping cultivation areas more consciously through sensor technology and spatial geodata, 3D printers (to help the country’s smallholder), production from side streams and waste along with main commodities, promotion and improvement of sustainable agriculture through innovation in drip irrigation, precision agriculture, and agro-ecological practices. Another advancement in the development of organic farming is BeeScanning App, through which beekeepers can fight the Varroa destructor parasite mite and also forms a basis for population modelling and breeding programmes ( Nova-Institut GmbH, 2018 ).

Inhana Rational Farming Technology developed on the principle ‘Element Energy Activation’ is a comprehensive organic method for ensuring ecologically and economically sustainable crop production and it is based on ancient Indian philosophy and modern scientific knowledge.

The technology works towards (1) energization of soil system: reactivation of soil-plant-microflora dynamics by restoration of the population and efficiency of the native soil microflora and (2) energization of plant system: restoration of the two defence mechanisms of the plant kingdom that are nutrient use efficiency and superior plant immunity against pest/disease infection ( Barik and Sarkar, 2017 ).

Organic farming yields more nutritious and safe food. The popularity of organic food is growing dramatically as consumer seeks the organic foods that are thought to be healthier and safer. Thus, organic food perhaps ensures food safety from farm to plate. The organic farming process is more eco-friendly than conventional farming. Organic farming keeps soil healthy and maintains environment integrity thereby, promoting the health of consumers. Moreover, the organic produce market is now the fastest growing market all over the world including India. Organic agriculture promotes the health of consumers of a nation, the ecological health of a nation, and the economic growth of a nation by income generation holistically. India, at present, is the world’s largest organic producers ( Willer and Lernoud, 2019 ) and with this vision, we can conclude that encouraging organic farming in India can build a nutritionally, ecologically, and economically healthy nation in near future.

This review work was funded by the University Grants Commission, Government of India.

None declared.

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Factors influencing the adoption of organic farming: a case of Middle Ganga River basin, India

• Published: 13 January 2023
• Volume 13 , pages 193–203, ( 2023 )

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• S. P. Singh 1 ,
• Priya 1 &
• Komal Sajwan 1  

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The sustainability of the agricultural system has become a global concern. Although the growth driven by green revolution technology has significantly contributed to making India self-sufficient in food production, the sustainability of the agricultural system has become debatable due to its adverse impact on the environment. Organic farming has become an alternative farming system to improve agricultural sustainability, yet farmers hesitate to adopt it. Therefore, this study aims to (i) identify the factors that affect the adoption of organic farming and (ii) investigate farmers’ perceptions towards its adoption. A total of 600 farmers (i.e., 300 organic and 300 conventional farmers) were randomly selected to conduct a field survey from two districts of the Middle Ganga River basin, India. A binary logistic regression was used to identify the factors that could affect the adoption of organic farming in the region. The results show that region, education, social category, training, farming experience, and monthly household income significantly affect organic farming adoption. Moreover, lack of financial support, lower yield levels, unavailability of markets, and expected low profits in organic farming are significant reasons that discourage farmers from adopting it. Therefore, by identifying significant variables associated with its adoption, the current study’s findings can provide better information for policymakers, which may help them make policies related to increasing the adoption rate among farmers.

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Professor S.P. Singh (first author) has received the funding from Indian Council of Social Science Research (ICSSR), New Delhi, India.

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Professor S.P. Singh has developed the idea for the current research and received the fund for the study. He assisted other authors in each and every step and finalized the draft of the manuscript. Priya has reviewed the relevant literature, analyzed the data and wrote the manuscript. Komal has helped in data collection and data analysis.

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Singh, S.P., Priya & Sajwan, K. Factors influencing the adoption of organic farming: a case of Middle Ganga River basin, India. Org. Agr. 13 , 193–203 (2023). https://doi.org/10.1007/s13165-022-00421-2

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DOI : https://doi.org/10.1007/s13165-022-00421-2

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Indian farmers' experience with and perceptions of organic farming.

Published online by Cambridge University Press:  20 June 2011

In India, the number of farmers converting to organic farming has increased in the recent past despite the lack of government support in providing knowledge and extension to the farmers. The aim of this article is to investigate the perceived relevance, benefits and barriers to a conversion to organic agriculture in three different Indian contexts—in Tamil Nadu, Madhya Pradesh and Uttarakhand states. In each state, 40 farmers from both organic and conventional systems were interviewed. The findings indicated that conventional producers identified production and marketing barriers as the main constraints to adopting organic farming, while the age and education of the farmer were not deemed a problem. Lack of knowledge and lack of institutional support were other barriers to conversion. Some farmers were, however, interested in converting to organic farming in the near future in Madhya Pradesh due to the low cost of production, and in Tamil Nadu and Uttarakhand due to the price premium and health benefits. On the other hand, organic farmers were more concerned with health, environmental and production factors when institutional support was available. The years under conversion were positively associated with reduced input costs in all three states and with increased income in Tamil Nadu and increased yield in Madhya Pradesh. Both organic and conventional farmers found the two production factors, low yield and pest control, to be of major concern. However, organic farms in Madhya Pradesh and Uttarakhand experienced yield increases because most of the farms were in the post-conversion period, while the farms in Tamil Nadu were in the conversion period and experienced yield reduction. The study suggests that the government scheme for compensating yield loss during the conversion period and a price premium may help farmers adopt organic agriculture on a large scale in India.

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• P. Panneerselvam (a1) , Niels Halberg (a2) , Mette Vaarst (a3) and John Erik Hermansen (a1)
• DOI: https://doi.org/10.1017/S1742170511000238

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• Farming Systems
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Organic Farming in India: Present Status, Challenges and Technological Break through

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Deep in the Weeds of Organic Farming

By Matthew S. Taylor, Mariëlle H. Hoefnagels, Mark E. Walvoord

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Deciding between organic or conventional produce is one of the many choices that consumers make when buying food. Is organic produce worth the extra cost? Is it healthy and good for the environment? This case study challenges students to gather evidence defending both organic and conventional farming. The case begins with a story in which customers at a farmers market talk to an organic and a conventional farmer. After reading the story, students in small groups use online resources to investigate claims about the environmental and health benefits of each farming practice. Students then use the structured method of “intimate debate,” taking turns defending both organic farming and conventional farming. In addition to applying ecological concepts such as competition and nutrient cycling to agriculture, this case study also helps students learn how to identify bias in online resources and engage in civil, substantive conversation about environmental problems. The case was designed for college students in an introductory biology course to reinforce scientific thinking near the beginning of the semester or at the end of the ecology unit to reinforce ecological connections.

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Date Posted

• Discriminate reputable sources of information from biased sources.
• Defend an opinion using scientific arguments.
• Explain the tradeoffs of conventional and organic agriculture.
• Talk to others about the pros and cons of buying organic foods.
• Define organic agriculture, pesticide, and fertilizer.
• Explain why weeds and pests limit crop growth.
• Describe how synthetic pesticides kill weeds and insects.
• List ways to manage weeds and insects other than with synthetic pesticides.
• Compare and contrast synthetic pesticides with organic alternatives.
• Recall which nutrients limit plant growth.
• Compare and contrast synthetic fertilizers with compost.

Organic farming; conventional farming; fertilizer; pesticides; compost; weeds; bias; debate; agriculture;

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High school, Undergraduate lower division

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TYPE/METHODS

Teaching Notes & Answer Key

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Barriers to the development of organic farming: a polish case study.

1. Introduction

2. materials and methods, 4. discussion, 5. conclusions, author contributions, conflicts of interest.

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Łuczka, W.; Kalinowski, S. Barriers to the Development of Organic Farming: A Polish Case Study. Agriculture 2020 , 10 , 536. https://doi.org/10.3390/agriculture10110536

Łuczka W, Kalinowski S. Barriers to the Development of Organic Farming: A Polish Case Study. Agriculture . 2020; 10(11):536. https://doi.org/10.3390/agriculture10110536

Łuczka, Władysława, and Sławomir Kalinowski. 2020. "Barriers to the Development of Organic Farming: A Polish Case Study" Agriculture 10, no. 11: 536. https://doi.org/10.3390/agriculture10110536

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Peer-reviewed

Research Article

An ecological framework to index crop yields using productivity and Ecosystem Fit: A case study from India

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, United States of America, Oak Ridge Institute for Science and Education (ORISE), Oakridge Associated Universities, Tennessee, United States of America

Roles Conceptualization, Methodology, Writing – review & editing

Affiliation School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, United States of America

Roles Conceptualization, Methodology, Writing – original draft, Writing – review & editing

Roles Conceptualization, Writing – review & editing

Affiliation Department of Civil and Environmental Engineering, University of Washington; Seattle, Washington, United States of America

Roles Conceptualization, Methodology, Visualization, Writing – review & editing

Affiliation Yale Pinchot Professor of Forestry and Environmental Studies Emeritus, 1500 SW 11th Ave. Unit 2304, Portland, Oregon, United States of America

• Angela M. Klock, 
• Amita Banerjee, 
• Kristiina A. Vogt, 
• Korena K. Mafune, 
• Daniel J. Vogt, 
• John C. Gordon

• Published: September 16, 2024
• https://doi.org/10.1371/journal.pstr.0000122
• Reader Comments

On the global scale, agricultural crop yields have decreased or plateaued over the last several decades. This suggests that the current focus on selecting crop varieties based on a plant’s light-use efficiency (photosynthetic and nitrogen-use-efficiency metrics) may not be sensitive to the site’s edaphic parameters, which limit growth. This study introduces a new framework to determine if crops can achieve higher yield potentials by assessing how plants adapt to the edaphic properties that impact growth, especially when contending with climate change. The new approach calculates an Ecosystem Fit index using a ratio of remotely sensed (or observed) total net primary productivity to the theoretical maximum productivity of the site. Then, it uses that index as a benchmark to judge quantitatively whether any new crop species or variety is improving potential biomass or economic yields at that specific site. It can also determine the best soil types for those crop varieties and monitor their potential adaptability relative to climate change over time. This study used a database of 356 spatially independent reference sites to develop this framework using a landcover classification of crops across 21 ecoregions and five biomes in India. It includes total net primary productivity data, theoretical maximum productivity potential, and soil and climatic data. This comparison showed that the light-use efficiency model, as intended, was not sensitive to variations in soil characteristics, temperature, or precipitation. Our framework showed significant differences in growth by soil type and precipitation and three significant productivity thresholds by soil type. The results of this study demonstrate that total crop productivity and Ecosystem Fit create a useful index for local land managers to assess growth and yield potentials across diverse edaphic landscapes and for decision-making with changing climates.

Author summary

Intensive farming practices emerged ~5,000 years ago to feed a growing human population. In the 1950s, the Green Revolution introduced fossil-fuel derived nitrogen fertilizers and pesticides to increase yields. Subsequently, plant photosynthetic- and nitrogen-use-efficiency models were used to assess yield potentials of crops and which varieties to plant across a diversity of agricultural landscapes. These contributed to globalizing agricultural productivity as yields increased but shifted crop selection to mainly utilizing nitrogen levels in the harvested product as the selection criteria. However, yields began to plateau or decrease globally in the 1990s, especially in developing countries. Also, a smaller percent of applied fertilizer nitrogen was found in the harvested product, while surplus nitrogen accumulated in the soil and polluted water systems via runoff or leaching. Using 356 site-level soil and climatic data from India, this study demonstrates that productivity measures allow managers to determine which management options increase the Ecosystem Fit of a crop given the site’s growth-limiting conditions. This shifts management options to optimizing total plant productivity based on local-edaphic and environmental growth-limiting factors. Thorough understanding of tradeoffs and feedbacks in crop ecophysiology will potentially help to reduce negative environmental externalities associated with agricultural production at the site level.

Citation: Klock AM, Banerjee A, Vogt KA, Mafune KK, Vogt DJ, Gordon JC (2024) An ecological framework to index crop yields using productivity and Ecosystem Fit: A case study from India. PLOS Sustain Transform 3(9): e0000122. https://doi.org/10.1371/journal.pstr.0000122

Editor: Semra Benzer, Gazi Universitesi, TÜRKIYE

Received: December 19, 2023; Accepted: July 24, 2024; Published: September 16, 2024

Copyright: © 2024 Klock et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data are in the manuscript and/or Supporting information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Efficient agricultural management and sustainable practices are needed to meet national food security demands. Today, more than 700 million people across the globe are estimated to be living in hunger [ 1 ] due in part to limited access to adequate amounts of food. Food insecurity at certain times throughout the year, or an inability to acquire adequate food due to lack of money or resources, is often considered one of the major causes of inadequate food production. This looming issue is further exacerbated by the increasing food and energy demands of a rapidly growing global population. Current estimates suggest the human population will increase from 7.6 billion to 9.7 billion by 2050 [ 2 ], and such an increase would require a 70% to 100% increase in the yield of major cash and commodity crops [ 2 – 5 ].

Expanding the land area in agricultural production is not a viable option to increase food production since the reserves of arable land are finite. Today, ~44.3% of the global habitable land area is already in agricultural use, 10.4% to grow crops, and 34.9% is designated as grazing land for animals or to produce animal feed [ 6 ]. The remaining 45.7% of land area is less suitable for farming because of poor soil quality, e.g., low soil organic matter levels, low water holding capacity, or low nutrient levels. Another example of why it may be hard to increase agricultural production in the United States is that Lark et al. [ 7 ] reported that 69.5% of new croplands in the United States already meet the average forecasted yield production. Further, current agricultural practices have polluted agrarian lands and left a legacy of damaged non-arable lands [ 8 – 9 ] with decreased biodiversity and loss of ecosystem services [ 4 , 10 – 12 ]. Therefore, converting previously farmed fields or non-arable lands into agricultural production would further contribute to soil and atmospheric pollution without appreciable gains in crop yields [ 8 ].

Another issue facing agriculture is that major food crops have reached the biological limit of increasing growth rates, as demonstrated by yields plateauing under current intensive agricultural management practices despite technological innovations in crop management [ 13 – 14 ]. Ray et al. [ 15 ] analyzed ~2.5 million global observations between 1961 and 2008 and found that yields increased in some areas but stagnated or collapsed in 24 to 39% of maize, rice, wheat, and soybean-growing regions. Similarly, Ritchie et al. [ 6 ] summarized global crop yields between 1961 and 2020, showing most have plateaued over the last decade, especially grain crops.

Historically, increasing yields resulted from management practices that optimized photosynthetic efficiencies and the application rate of nitrogen fertilizers to maintain higher photosynthetic rates and improve nitrogen uptake efficiency. Since many studies initially supported increasing yields by applying synthetic fertilizers and pesticides and irrigating arable lands [ 14 ], these were reasonable management approaches to help increase yields. However, these practices were not holistic and eventually became counterproductive as they degraded soil quality over time [ 14 ]. They did not factor in the importance of soil types and health on a plant’s productive capacity and ability to adapt to regional soils and changing climates.

Soil quality is an integral factor that should be incorporated into helping increase crop yields [e.g., 7 , 14 , 16 ]. For example, Fan et al. [ 14 , 16 ] reported that low-productivity soils reached yields of <1,500 kg ha -1 while high-productivity soils had five times higher yields (>7,595 kg ha -1 ). They attributed these greater yields in the high-productivity soils to the quality and health of the soil. Also, Fan et al. [ 14 ] suggested that a lack of organic matter content of the low- and high-productivity soils would have to be alleviated to increase crop adaptation to their site; both soil types had 25 to 50% less soil organic matter than arable soils in European countries and the U.S. [ 14 ]. Further, Jiao et al. [ 16 ] wrote how high-quality soil increased the resilience of cereal crops to climate change variability and improved yields by 0.5 to 4.0% compared to low-quality soils. The Fan et al. [ 14 ] study highlighted the importance of selecting crops that can phenotypically adapt to the soil and micro-climate while overcoming any growth-limiting conditions unique to the site, especially under a changing climate scenario. It also supported the need to manage soil quality and recognize each location’s inherent constraints beyond the resource delivery capacity of the soils.

Gaps in assessing crop yields by focusing on photosynthetic and nitrogen-use-efficiencies (NUE)

Crop genotypes with high photosynthetic and nitrogen-use-efficiencies (defined as the ratio of the nitrogen taken up by a crop to the total input of fertilizer nitrogen) are the preferred plants to grow since the assumption was that the total amount of carbon fixed by a plant will determine its potential yields, especially when nitrogen fertilizers are applied in sufficient quantities to maintain high photosynthetic rates [ 17 ]. Research has supported these assumptions. For example, Li et al. [ 18 ] reviewed 130 publications that assessed data on yield, shoot biomass, and nitrogen concentration that suggested the genetic transformation of crops (rice, maize, wheat) impact their nitrogen-use-efficiency (NUE) in different soil fertilization conditions. The latter study showed that genetic improvement in NUE significantly increased the grain yield of crops. However, this study also reported that potted experiments have a higher yield variance than field-grown crops [ 18 ], which suggests that other factors in field experiments limit plant growth and carbon and nitrogen assimilation efficiencies. Thus, the standard metrics to estimate potential yields are not sensitive predictors of growth rate changes under variable field conditions and climates.

Today, focusing on applying nitrogen fertilizers to increase photosynthetic efficiencies is seldom attainable by itself since ecophysiological and site factors limit a crop’s NUE. Simkin et al. [ 12 ] described the challenge of feeding the world by needing to increase yields by 40% through improvements in photosynthetic efficiency since “… as much as 50% of fixed carbon is lost to photorespiration…” Gutschick [ 19 ] suggested that respiration should become a focus of increasing yields since two-thirds of the original photosynthate goes into maintenance and operational costs during a grain crop’s entire growing season. Managing respiration to increase growth rates by reducing photorespiration; however, ignores its other essential role in plant physiological functions. For example, plants must continue to produce antioxidants to protect against reactive oxygen species when excitation energy cannot dissipate during drought as stomata are closed to conserve water [ 20 ]. This supports that there are limits to how much science can manipulate a crop’s genotypic potential without resulting in unintended consequences on yields. Further, these studies support what Boyer [ 21 ] wrote in 1982 that a crop only reaches 30% of its genetic potential.

Since the genetic potential of a crop results in a plant specialized to grow under specific site growth conditions, plants are less able to adapt to growth conditions that change due to unpredictable temperature and precipitation regimes. Farmers need to select crop plants capable of growing in specific soil and micro-climate niches but also to possess the traits to adapt to short-term changes in environmental conditions, e.g., phenotypically adapting by allocating energy to plant parts such as roots to acquire a growth-limiting resource such as water during short-term droughts. Plants that are specialists in adapting to site-level conditions must also be generalists adapting to unpredictable temperature and precipitation regimes.

Rizzo et al. [ 22 ] used an extensive database from 2005 and 2018 to show a plant’s genetic potential explained the smallest fraction (13%) of crop yields and management practices explained 29% of the yields in Nebraska, United States (13% of the increased yield was associated with addressing a crop’s genetic potential, 48% were correlated to decadal-level climate change, while agronomic improvements in management explained 29% of the yield increases). In industrial farming, a crop’s genetic potential allows it to efficiently grow in a specific site-level condition, while a crop’s phenotypic potential is met by farm managers, not the plant, who become the adaptive agents mitigating a plant’s inability to adapt to a changing growth environment by fertilizing, irrigating or applying pesticides [ 22 – 28 ]. The Rizzo et al. [ 22 ] study suggested that farmers can manage about 42% of the factors explaining yield levels, while climate change accounted for almost half of the yields reached by crops and has been the most problematic to manage.

Since multiple variables may explain significant changes in yields, it is challenging to identify which variables or combinations of variables describe changes in crop yields without introducing other unintended limitations to growth or environmental pollution [ 29 – 30 ]. Others recommended studying NUE more holistically, including the process of the crop acquisition of soil nitrogen. For example, Govindasamy et al. [ 26 ] suggested the initial increases in NUE were due to indigenous soil fertility levels increasing N uptake by crops. Improving NUE continues to be a focus to increase crop yields due to high N fertilization applications, which frequently results in water eutrophication and soil pollution [ 26 , 28 , 31 – 32 ]. Congreves et al. [ 33 ] reviewed NUE definitions and indices and what is currently ignored in the traditional index, such as “accounting for a wider range of soil N forms, considering how plants mediate their response to the soil N status, including the below-ground/root N pools, capturing the synchrony between available N and plant N demand, blending agronomic performance with ecosystem functioning, and affirming the biological meaning of NUE.” Therefore, focusing on only one or two variables to help increase yields across large heterogenous landscapes will probably not be efficient since other edaphic and environmental factors will become important controlling factors of crop growth within a large landscape, affecting the yields disproportionately.

It would be essential to understand and include a plant’s C allocation to growth and maintenance functions to assess the efficacy of an assessment protocol to measure changes in a plant’s NUE. For example, Asibi et al. [ 32 ] wrote how the overuse of N fertilizers resulted in low NUE when there was no simultaneous accounting for increased water-use efficiency. Cassman et al. [ 24 ] wrote, “Trends in NUE and the cultivated area will ultimately determine global N fertilizer requirements and the risk of N losses to the environment.” Currently, the dominant factors that increase potential crop yields are selecting cultivars through genetic improvements and improving NUE and water-use efficiencies while decreasing the negative impacts of high fertilizer application rates [ 24 , 34 ]. And importantly, over the last five decades, forest research has provided insights into how plants phenotypically adapt to N fertilizer additions in natural environments [ 35 ], which could help frame crop research.

It follows then that modifying NUE by adding N fertilizer to the soil to increase crop yields should involve holistically monitoring soil health for any degradation, subsequently leading to decreases in crop yields. For example, Fan et al. [ 14 ] and Jiao et al. [ 16 ] wrote how fertilizer applications in China increased from 1 t ha -1 in 1961 to 6 t ha -1 in 2015, resulting in increased grain productivity and grain yields in about half of the countries’ arable land area. But, to reach these yield increases, China consumed 35% of the global fertilizer and increased arable land irrigation by 32% while becoming the second-largest producer and consumer of pesticides, accounting for 14% of global use [ 14 ]. Despite fertilizer applications, irrigating crops, and controlling pests and pathogens, the yields of cereal crops decreased in China from 4% in the 1970s to 1.9% in the 1990s [ 14 ]. They wrote that the amount of nitrogen recovered in the aboveground crop biomass was 35% in the 1990s but declined to 28.3% for rice, 28.2% for wheat, and 26.1% for maize, all these values are lower than world averages of 40–60%. China represents an example of the inevitable holistic trade-offs that need to be anticipated. That is, yield optimization means that there will probably be interactive effects on other, potentially unknown, agricultural economics and ecological processes that would affect future yields.

The introduction of synthetic N fertilizer applications by the Green Revolution were essential to achieve high crop yields; however, understanding how N fertilizer applications impact crop growth holistically is incomplete because crop management and efficiency are based mostly on monitoring harvested product yields and not total plant productivity or edaphic conditions. For example, plant growth rates can significantly increase when soil N levels are low [ 36 ] because plants phenotypically adapt by growing more roots.

Further, N-fertilized crops need a higher application rate of pesticides to protect the yield [ 37 ] since C allocation to defensive plant chemicals decreases. Fürstenberg-Hägg et al. [ 38 ] reported that plant defenses and insect herbivory pressure have metabolic costs. The plant must produce physiologically expensive defensive chemicals using photosynthetically fixed C, reducing its growth and development. (see Martinez et al. [ 37 ] for a review of all the links between N fertilizer and pesticide applications and their unintended impacts on ecosystems, wildlife, and people). The C allocation trade-offs that a plant experiences are highlighted by addressing a plant’s response to herbivory. These are not reflected in the total photosynthate produced but are part of the within-plant C allocation patterns directly impacted by N fertilizer applications. When a plant needs to defend itself, it does not grow more roots to adapt to a drought or acquire more nutrients. These trade-offs, therefore, support understanding and managing the source-sink relationships of a total crop and not just the harvested product, which better reflects the relationship of a plant’s phenotypic plasticity to its soil and environmental conditions.

Science-practice gaps in assessing crop yields: Source-sink relationships

The harvest index in seed-producing crops is a C-centric approach that dictates that total shoot dry matter determines aboveground “sources” of photoassimilate, and harvested grain represents the “sinks” [ 27 ]. The harvest index also has a C-centric view of yield despite the variation in yields arising from the diversity of soil and climatic environments in which the crop grows. As the harvest index varies with differences in crop management [ 39 ], selecting a harvest index likely guarantees a high yield potential only under the environment for which it is selected to plant. The success of this approach at the local level will require managerial diligence and high effort while also being capable of flexibility in the face of climate change. Ultimately, crops are planted in diverse soil and environmental conditions, suggesting a crop cultivar may not perform well in many areas where it is grown. The interaction between harvest index and ecological variation in the growth environment is complex and may not scale according to total yield. However, since harvest-index increases are limited by source and sink strengths, these relationships may provide a valuable tool to identify which cultivars would grow best under different soil and climatic conditions.

The harvest index has a theoretical maximum, and there is a level at which a plant needs to grow more shoot biomass to achieve higher yields [ 40 ]. To optimize crop yields, each plant must produce leaves and roots to capture light and assimilate water and nutrients to form the stem to support the leaf canopy, especially flowers and grain. For example, leaf photosynthesis strongly correlates with increased foliar and total plant biomass [ 41 – 42 ] but less with how a plant allocates carbon since crops are generally selected for higher yields and lower root biomass [ 43 ]. A study conducted in the Midwest United States reported that maize had 8.3% of its total biomass in roots and 52.1% in grain yield at maturity, while soybeans had 16.6% in roots and 41.0% in grain yield [ 43 ]. The high percentage of grain yields does not represent a crop with an evolutionary balanced source-sink relationship where crops adapt to their growth environment. It also demonstrates how selecting traits to maximize crop yields reduces the crop’s ability to adapt to its changing growth environment phenotypically. Climate change may disrupt other normal growth conditions besides temperature and precipitation. It includes novel stressors such as high winds, which would drive carbon allocation to stems or root biomass or increase secondary metabolites in response to pests or other complex combinations of unique factors [ 44 ].

A greater understanding of the source-and-sink relationships of the plant facilitates understanding how a plant shifts its allocation to source and sink functions following fertilizer application and whether a plant can adapt to climate change at the site level. This is not just the total amount of C that is fixed and allocated to the harvested product but whether sufficient C is allocated to nutrient and water uptake or to defensive compounds if pests or pathogens attack [ 35 , 45 – 46 ]. Today, meeting the challenge to improve crop productivity requires increasing our understanding of total crop growth (above- and belowground) on different soil types and the impact of environmental stressors such as climate change on crop yields. There will be unique combinations of conditions that exhibit dynamic interactions at the site level that will drive the ecological response ( Fig 1 ). This is especially important to include in a framework assessing how to improve crop yields where, for example, irrigation may increase yield, but N fertilization may decrease yield. Decreased yields resulting from N fertilization may represent changes of the within C allocation fluxes in plants, e.g., between growth and maintenance to above- and below-ground plant parts (e.g., fruit, seeds, leaves, roots, and defensive chemicals). For example, Baslam et al. [ 29 ] explained how increasing N levels might reduce crop yields as plant shoot growth increases with less C allocated to roots.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

[ 1 ] Sources; the dominant factors that influence nutrient availability, [ 2 ] Controls; genomic mechanisms of plant adaptation to disturbance in its environment, and [ 3 ] Sinks; plant carbon sink strength as a function of metabolism. The major functional processes associated with each listed at bottom. The plant images are from unknown artists; (left) maize plant (Gong F, Wu X, Zhang H, Chen Y and Wang W (2015) Frontiers | Making better maize plants for sustainable grain production in a changing climate (frontiersin.org ) and (center) phenotypic plasticity of roots (Calleja-Cabrera J, Boter M, Oñate-Sánchez L and Pernas M (2020) Frontiers | Root Growth Adaptation to Climate Change in Crops (frontiersin.org ) are both licensed under CC BY . The chemical structure of the secondary metabolite was recreated by the author.

https://doi.org/10.1371/journal.pstr.0000122.g001

Measuring changes in photosynthesis may not detect changes in plant growth due to drought, low temperature, and nutrient limitations [ 47 ]. For example, a drought will inhibit cell growth and tissue formation before C uptake is inhibited during photosynthesis. Also, meristem cell production will stop at temperatures ≤ 5°C, while net photosynthesis continues at 50–70% of the rate when plant growth occurs above 5°C [ 47 ]. Körner [ 47 ] supports understanding how above- and belowground growth changes in relationship to drought, low temperature, and nutrient limitations since these variables impact plant growth much earlier than photosynthetic efficiencies. Holland et al. [ 48 ] further suggested using a theoretical analysis approach to explore how root and leaf respiration can explain C allocation strategies by increasing the timing of C assimilation to leaves and roots. Their study supports using models to understand growth and recognize that maintenance costs can also enhance yields.

These studies support the need for more research on the interplay of physiological processes, such as how allocation shifts between the shoots and roots are involved in increased water and NUE, while also decreasing pollution from high fertilizer applications. Most experimental field studies emphasize taking measures of seedlings under controlled conditions and modeling crop yields that do not include realistic estimates of how crops allocate C to all the C pools and fluxes [ 49 ]. For example, selecting a crop to become more drought tolerant may reduce its yields as there is insufficient C fixed during photosynthesis to balance all the source-sink relationships that are the adaptive mechanisms in response to climate change.

Ecosystem translation of the belowground world to illuminate potential crop yields

If improving photosynthetic efficiency and NUE successfully address all the growth-limiting factors in agriculture, crop yields will probably not plateau or decrease in many parts of the world [ 6 ]. Photosynthetic efficiency is crucial since it determines the size of C pools available for improving crop yields. However, within plant source-sink relationships will determine whether a crop can grow and maintain its tissues and adapt to increase its assimilation of resources that limit its growth. Thus, it will decide if a crop will reach its growth and yield potential [ 27 ]. Roots are essential in determining how much water and indigenous soil nutrients and N applied in fertilizers are assimilated by a crop plant. Also, roots are the interface exposed to moderately-to-severely degraded land and determine whether crops can grow on a site; Iseman and Miralles-Wilhelm [ 50 ] reported that 52% of the global agricultural soils are moderately to severely damaged.

There is increased recognition of the importance of roots in crop adaptation to their changing environment and that N fertilizer decreases root growth [ 34 , 45 , 51 – 54 ]. Previously, the assumption was that knowledge of aboveground growth is a surrogate for the total plant response to its dynamic environments. However, Liu et al. [ 54 ] synthesized 88 published studies to show the existence of a phenological mismatch in the timing of above- and below-ground growth in response to climate variability. Therefore, we must implement a holistic view of the plant adaptive response at multiple levels of biological organization, from resource allocation to phenotypic growth patterns, and more fully relate the emergent properties that arise under different environmental and ecological conditions to the individual agricultural system components.

Further, the increased frequency of droughts is impacting yields. It highlights the role of roots in a crop adapting to short-term changes in available water supplies and nutrient acquisition. When less photosynthate allocation to roots occurs with higher N applications [ 35 , 55 – 56 ], a plant may produce less biomass, i.e., lower productivity levels, since plants with smaller root systems have less capacity to acquire soil nutrients and water during a drought. Maslard et al. [ 53 ] described how selecting for a diversity of optimal root systems in new soybean varieties is important to address edaphic and climatic limits to crop growth, e.g., cultivars with deep root systems to take up N and water from lower soil layers and cultivars with shallow root systems that can readily acquire nutrients from surface soils such as when phosphorus availability is limited. The total biomass of nine different soybean genotypes by Maslard et al. [ 53 ] had statistically significant differences in total plant, shoot, and root biomass, showing how selecting a crop by its root biomass is helpful information for managers.

Olagunju et al. [ 52 ] reported how tropical upland rice adapts to dynamic climatic conditions such as unpredictable rainfall and the resulting droughts by reducing biomass allocation to shoots but not to roots. They further showed that upland rice adapted to these periodic drought events depending upon how soil texture impacted root and shoot growth, i.e., less root biomass is produced as the amount of clay increases in the soil. Olagunju et al. [ 52 ] wrote that focusing on the plant’s reproductive parts would result in “more reliable estimates for identifying rice cultivars with higher yield potential at harvest.” They also wrote, “Soil environment that promotes greater allocation of biomass to reproductive structure through a restriction in the expansion of vegetative organs is well suited for upland rice cultivation.” A focus on selecting cultivars for their reproductive yields makes the crop susceptible to the unpredictable rainfall periods that a balance of root biomass and crop yields would not produce.

In addition to observed responses, climate variability may also induce unpredictable responses that will vary by the local milieu of growth conditions. For example, higher soil temperatures are associated with less allocation to root biomass as feedback to changes in soil condition, including loss of moisture, aeration, and nutrients [ 56 ]. Calleja-Cabrera et al. [ 45 ] wrote about the need to develop an efficient root system that can make a plant better adapted to its site to increase crop productivity. They wrote how increasing temperatures with climate change increases the stresses that a crop will experience and will have to adapt to, e.g., “drought, salinity, nutrient deficiencies, and pathogen infections” [ 45 ]. In that case, a crop plant that can adapt to its growth environment should be selected, which means data needs to be collected on total plant biomass and carbon allocation to roots and defensive compounds [ 35 , 46 ]. This selection process must account for different types of variability, the magnitude of change among the most important growth parameters, and how well the cultivar is expected to respond across a diversity of conditions. These carbon allocation shifts in response to site edaphic and micro-climatic factors ultimately determine how well a plant grows at a local site and how much crop yields can increase when growing under dynamic environmental conditions [ 35 , 57 ].

Cakmak et al. [ 58 ] wrote how a balanced fertilizer application is needed to maintain growth since mineral deficiencies of phosphorus, potassium, and magnesium impact C partitioning differently between roots and shoots in bean plants. Their study showed the roles of magnesium and potassium in allocating C from shoots to the roots. Woo et al. [ 34 ] showed how managing wheat yields to achieve a root radius of 0.1 and 0.3 mm resulted in optimal wheat yields. Since many other factors impact roots, managing fertilizer application rates may not support root growth architecture in the field [ 34 ].

Instead of focusing on the product’s increased yield, a farmer can select a crop based on variable rooting depths to address site scale limitations to growth during dynamic climatic conditions. The importance of selecting genotypes based on their root architecture and drought tolerance, as well as the role of roots in increasing soil organic matter levels, is stimulated by the recognition that “allocation pattern indicates environmental plasticity to soil properties, temperature and soil water availability” [ 51 ]. Mathew et al. [ 51 ] reported how drought stresses reduced total biomass production by 35% and root-to-shoot ratios by 14% and how soil C is mainly derived from root activity and decomposition of root tissues. These are subtle and specific observations of the plant response, and as we learn which responses to measure, we will fine-tune the site-level accommodations required and the selection of varieties that can meet the specific demands of the site.

Selection of plants based on their root architecture still needs to factor in the continued use of fertilizers in farming. This means that when a plant is bred for its increased allocation of photosynthate to the harvested product, it may be less able to maintain and protect plant tissues or acquire other limiting resources needed to grow [ 59 ]. This occurs when a crop is selected to optimize one part of the plant, e.g., genotypes high in seed oil and protein content for human consumption, animal feed, transport fuels, and many other products. Under these circumstances, the plant allocates less to defensive chemicals. The farmer must spray herbicides and pesticides to reduce the growth of other competitive plants and to protect the plant due to the tradeoffs that were considered acceptable to management when those protective traits were discounted for the artificially selected desired traits.

A holistic framework: Thresholds of total productivity and yields due to growth-limiting factors

However, one still needs a framework to determine how much site growth limiting factors reduce a crop’s total productivity to determine the potential growth and yields possible per site. This concept supports using a metric—e.g., a plant’s total productive capacity—to estimate whether a crop is close to a threshold of decreasing productivity with additional growth-limiting factors. This index assesses a plant’s growth in its edaphic-climatic environment and allows cross comparisons of different sites [ 55 ]. A module, like Ecosystem Fit (eFit), must be created to assess whether a plant can continue adapting its potential productivity in response to site growth-limiting conditions. Since eFit uses parameters of solar radiation, temperature changes, edaphic and climatic factors, it may be useful to reflect or index growth rates or site productivity and compare it to other sites with different conditions. In fact, indices like this could be used to tease apart specific site factors that affect productivity.

A farm needs to be viewed at an ecosystem-level and based on total productivity measures, especially since crop yields may be a third of the total C fixed during photosynthesis [ 22 ]. A plant’s total net primary productivity (tNpp) represents how much C can be allocated to acquire growth-limiting resources, such as nutrients and water, in dynamic soil and climatic conditions. In addition, the crop adapts to grow and store carbohydrates to protect itself during disturbances. A crop plant should not be decoupled from its growth environment since management will not be able to address all the limitations to growth that a crop plant will experience. This suggests monitoring photosynthetic efficiencies, and NUE needs to be replaced with a holistic approach that assesses the entire ecology of the crop. We currently don’t have the frameworks to assess how management can counter yield decreases locally, especially since crop growth is generally viewed through a narrow lens that looks at only part of the aboveground portion of the plant. This narrow focus does not include site-level soil and climatic factors which are crucial for understanding the totality of factors that explain decreases holistically at the local-level. For example, it does not factor in the potential negative effect of high levels of N fertilizer which can decrease the plant’s ability to acquire nutrients from unhealthy soil.

The new framework to measure the phenotypic plasticity of a plant first emerged from Gordon et al. [ 60 ], who used the estimated Theoretical Maximum Productive Capacity (TNpp max ) using the Loomis and Williams [ 61 ] method. Gordon et al. [ 60 ] developed a conceptual approach to calculate the eFit or the potential total productivity for a site based on the ratio of field-collected data of tNpp to the TNpp max calculated from external factors. Gordon et al. [ 60 ] described eFit, and Klock et al. [ 35 , 62 ] provided the methodology to calculate theoretical maximum productivity and eFit. In global forests, eFit and total productivity were invariant to leaf behavior traits but were “strongly dependent on temperature, precipitation, elevation, Oxisol, Entisol, and Ultisol soils, and silty loam soil texture” [ 35 ].

Gordon et al. [ 63 ] demonstrated the utility of using tNpp to calculate eFit on two crop plants grown in Japan. They calculated an eFit of 19% for maize in Iwate, Japan (actual tNpp of 18 Mg ha -1 yr -1 ) and 14% for rice (not displayed) in Akita, Japan (actual tNpp of 15 Mg ha -1 yr -1 )( Fig 2 ). In the eFit calculations made by Gordon et al. [ 63 ] and the yield data of ~5 Mg ha -1 yr -1 dry weight provided in Ritchie et al. [ 6 ], about 25% of the tNpp is allocated to the harvested product, and the remaining to plant maintenance and growth. Ecosystem fit showed that both maize and rice reached less than 19% of their theoretical maximum productivity threshold (TNpp max ). Gordon et al. [ 60 ] also showed how eFit is sensitive in describing how edaphic and climatic site conditions limit crop growth and yields, which are factors that can be managed or mitigated.

Maize in Japan using data published in Gordon et al. [ 60 ]. The potential productivity shows how much tNpp needs to increase to achieve a higher fit when ecological management alleviates the growth-limiting factors constrained by soil health and climatic factors. [Calculate eFit = tNpp observed/TNpp max * 100] [Globally, dry weight yields of maize are about 5 Mg ha -1 yr -1 [ 6 ] which is ~ 25% of total actual net primary productivity (tNpp).]

https://doi.org/10.1371/journal.pstr.0000122.g002

Definitions of yield and productivity

There is confusing terminology in the literature on what is included in the terms “yield” and “productivity” of a crop. Yields are generally related to agricultural production but are not synonymous with productivity that ecologists would use. For example, yields are the usable measured biomass or weight or also volume of the crop harvested, such as the grain, cereal, and fruit produced per unit of land [ 64 ]. Further, if yields are given in weight terms, the weights are moist weight values and are reported as a standardized moisture content, which is important for proper grain storage and comparison across other research trials [ 65 ]. It does not include the weight of any other part of the plant. The actual yield on a farm varies depending on the amount of sunlight or radiation reaching the plant, the plant’s water and nutrient uptake efficiencies, the crop’s genetic potential, and how much pests and pathogens decrease the crop product harvested.

Yield data does not provide as much information as the tNpp of the whole plant based on dry weight measures of biomass. For example, the productive capacity of a plant can indicate whether it has the potential to adapt to its dynamic environment and grow biomass at the site level. Additionally, since fertilizer applications generally decrease [ 55 ] the C allocated to the root system, it would follow that just being attentive to increasing yields would not allow managers to recognize that potential decreases in root biomass could significantly increase the risk of the crop’s ability to respond to lower soil moisture during droughty conditions and decreasing its yields. This would be even more important to consider for perennial crops. The question is whether long-term yields can be improved by selecting crop varieties based on their tNpp while balancing its allocation of C to the harvested product and allowing a plant to adapt to site-level edaphic and micro-climatic constraints and continue to grow.

In this paper, we will consider agricultural yields in terms of product biomass, but they are also known in terms of economic returns and produced on a per-unit-of-land basis [ 64 ]. It differs from total crop productivity (e.g., tNpp) measured by ecosystem ecologists as Mg ha -1 yr -1 , which represents a plant’s annual total biomass productivity. The ecological definition includes the plant’s above- and belowground parts (e.g., leaves, branches, stems, bole, coarse roots, fine roots, mycorrhizas), and biomass loss due to herbivory, autotrophic respiration, and root turnover and litterfall (e.g., carbon fluxes) added back to biomass carbon pools (e.g., [ 66 – 67 ]).

Knowledge of a crop’s actual tNpp and TNpp max will provide a framework to correlate yields to changing environmental and edaphic conditions represented across a heterogeneous landscape. Actual tNpp, in dry-weight biomass produced during a year, provides an index of growth that can be compared across a diversity of sites. In this paper, we present different productivity terms that are used in our framework. Actual Productivity of a crop is the total Net Primary Productivity (tNpp observed or measured) reached at a site as constrained by soil health, pests and pathogens, drought, temperature, and salinity, to name a few variables. The Potential Productivity (tNpp potential) is the amount that tNpp can be increased using management, i.e., environmental and/or ecological tools, such as those that can enhance or create healthier soils or use more adaptable plants for site-specific conditions. This is determined from field-based research that monitors tNpp changes under different management conditions. Calculating the theoretical maximum productivity (TNpp max ) provides the maximum productivity attainable at a given site, but it is not environmentally or ecologically possible to manage since it would involve managing limiting-growth variables that cannot be economically manipulated. Examples of the variables used to calculate TNpp max for a given site include solar radiation, temperature, and the growing season length, which are factors we cannot manage.

Characterization and definitions of the dominant soils in India (UN FAO)

• Cambisols—are young soils, medium and fine-textured, shallow topsoil depth; moderate fertility but commonly deficient in phosphorous (P) and calcium (Ca); high erosion rates, generally good water-holding capacity, good internal drainage, but the dried soil surface becomes extremely hard when dry, hindering root growth and favoring erosion.
• Fluvisols—are young soils forming in alluvial deposits with little or no profile development, mineral soils conditioned by topography; in coastal areas, they have high levels of salts and aluminum (Al) ions—therefore, low soil pH (i.e., high soil acidity) and high Al toxicity both help create phosphorus deficiency and also N deficiency is common; found in floodplains so they periodically flood and therefore need flood control, drainage or at times even irrigation.
• Luvisols—are moderately weathered soils, and if they have clay-enriched subsoils, they have high cation exchange capacities and high base saturation; steep slopes are not uncommon and need erosion control; high nutrient content, therefore are fertile and widely used for agriculture; well-drained but soil may become saturated with water for extended periods potentially needing drainage.
• Nitosols—are strongly weathered soils but are more productive than most red tropical soils and are deep soils with favorable physical properties with deep rooting so they are resistant to erosion; contain low-activity clays (i.e., have a lower capacity to retain and supply nutrients), high P fixation enhanced by iron/aluminum (Fe/Al) chemistry; generally fertile soils despite low available P and low base status but need to add P fertilizer; plant available nutrients are fairly deep (~150 cm), they are well-drained but total moisture storage is good because they are deep; however they are hard when the soils are dry. They are exploited widely for plantation agriculture.
• Vertisols—have high content of shrink and swell clays that are strongly impacted by wet and dry conditions (i.e., harden when dry and become sticky when wet), little textural differences by depth; good for mechanized farming if the rainfall is high or they are irrigated; many areas are not farmed because they would need to be irrigated; low soil permeability, so irrigation may cause waterlogging. They are best suited for pastureland use and cultivating plants, such as rice, that thrive in standing surface water.
• Xerosols—are desert soils that have mostly sandy soil and are in low rainfall areas; so, they have low N and organic matter (OM) but high concentrations of calcium carbonate and soluble salts and phosphate, therefore they are frequently infertile requiring substantial management. Generally, they have soil moisture deficits and are susceptible to wind erosion, so they are unsuitable for rain-fed agriculture. But if irrigated, these soils may be among the best soils for farming. Generally, these soils are of little or no value for agriculture due to the lack of rainfall.

Source: https://www.britannica.com/science/soil/FAO-soil-groups , https://www.isric.org .

Comparison of the dominant soil groups, mean annual precipitation, mean grow temperature, and Theoretical Maximum Productive Capacity (TNpp max )

The seasonal grow temperature was strongly associated with latitude ( r = –0.67, t = –16.874, df = 354, p < 0.001) whereas TNpp max was independent of latitude ( r < 0.001, t = 0.002, df = 354, p = 0.99), even though the latitudinal range of this study was ~25° (8.31° N– 32.80° N), nor was TNpp max associated with longitude ( r < -0.075, t = -1.777, df = 354, p = 0.16) where the range was ~19° (69.47° E– 88.59° E).

Unsupervised cluster analyses identified a single threshold for grow temperature of 26° C and two relatively homogenous clusters of TNpp max , one on either side of 289.1 Mg ha -1 yr -1 , thus data summaries were made for two TNpp max groups, classified as low (≤ 289.1 Mg ha -1 yr -1 ) and high (> 289.1 Mg ha -1 yr -1 ). We then utilized a series of parametric and non-parametric approaches to understand the association between TNpp max , tNpp, and eFit and dominant soil groups, and climatic variables.

The statistical power of the low and high TNpp max groups to soil type was low for a meaningful comparison ( power < 0.24), therefore we aggregated TNpp max . An omnibus Welch’s heteroscedastic F Test for the data ( n = 356) with post hoc Bonferroni correction for multiple pairwise comparisons found differences of at least one soil group with an effect size (ω 2 = 0.35 CI 95% [0.18, 1.00]) considered moderate per Cohen’s 1988 convention [ 68 ] ( Fig 3 ). As variability of TNpp max among the dominant UN FAO soil types was not correlated with geography, we fitted a generalized linear model (estimated using ML) to predict TNpp max with dominant soil type to identify which soil types were the drivers of the association ( Table 1 ). The model’s total explanatory power was low ( R 2 = 0.14) with an intercept corresponding to Cambisols of 291.91 (95% CI [290.2, 293.7], t (350) = 324.09, p < .001). Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p -values were computed using a Wald t-distribution approximation.

The large dot is the mean and reported adjacent to the plot. The thick horizontal line is the median. The box in the center represents the interquartile range, and the vertical thin gray lines above and below the box represent the rest of the distribution. The grey dashed line represents the grand mean. The totality of the curved shape represents a kernel density estimation where wider sections represent a higher probability of a given value and thinner sections represent a lower probability. Results of an omnibus one-way ANOVA, partial effect size, and number of observations reported at the top.

https://doi.org/10.1371/journal.pstr.0000122.g003

https://doi.org/10.1371/journal.pstr.0000122.t001

Another approach was to determine whether TNpp max varies among the dominant soil types by grow temperature. The estimation of TNpp max is based on incoming solar radiation and the mean growing temperatures for the total month-days with temperatures above 0°C. Therefore, the maximum theoretical potential for growth, i.e., TNpp max may be affected by environmental factors such as elevation or azimuth of a given site, indicating some amount of top-down control by topography.

There was a significant negative relationship between TNpp max and the grow temperature suggesting reduced crop productivity as temperatures increase among Luvisols, Nitosols, and Vertisols ( Fig 4 ). The soils that showed no relationship with temperature are also less suitable for agriculture due to nutrient deficiencies (Cambisols, Fluvisols) and low precipitation (Xerosols). The variability of TNpp max was highest among Luvisols ( var = 93.9), Fluvisols ( var = 121.4) and Nitosols ( var = 132.1), while it was lowest for Cambisols ( var = 29.4), Xerosols ( var = 35.7) and Vertisols ( var = 51.1) ( Fig 4 ).

Association between TNpp max to the average grow season temperature among each dominant soil type. A temperature threshold of 26° C is indicated by the dashed red line, and the Pearson’s Product Moment correlation ( r ) with p -value is reported at the top for each soil type.

https://doi.org/10.1371/journal.pstr.0000122.g004

Comparison of TNpp max to mean annual precipitation indicated how the dominant crop growing areas varied between precipitation levels (i.e., dry to wet precipitation groups). Half of the dominant soil types indicate non-significant relationships, therefore maintaining a focus on selection for crops with higher photosynthetic efficiency is not going to provide additional productivity benefits ( Fig 5 ). Most notably, TNpp max was primarily concentrated in a range of ~280 to 300 Mg ha -1 yr -1 , but above a threshold of ~1,200 mm of annual precipitation, there was increased variability of TNpp max among Fluvisols and Nitosol dominated sites probably due to specific climatic niches. In contrast, crops growing in Cambisols, Luvisols, Vertisols, and Xerosols are located in areas that have high TNpp max , >300 Mg ha -1 yr -1 . Xerosols and Cambisols were the only soil groups identified in the dry precipitation group ( Fig 5 ).

Four precipitation classes; dry, moist, very moist, and wet mean annual precipitation are from Vogt et al. [ 69 ]. The optimal range of precipitation for agricultural productivity is indicated by the grey panel (500–1200 mm -1 yr -1 ). Pearson’s Product Moment correlation ( r ) with p -value is reported at the top for each soil type.

https://doi.org/10.1371/journal.pstr.0000122.g005

This comparison between TNpp max and annual precipitation further supports the need to characterize the site-level edaphic and micro-climatic conditions since total productivity is related to the available water supplies and the nutrient delivery status of the soil. The highest variability in TNpp max was found in the very moist group, ~1,200 mm yr -1 precipitation, where TNpp max showed a bifurcated response. For example, Ferric Luvisols had a linear decline and linear increase, whereas crops growing on Chromic Luvisols would be limited in available energy, limiting their adaptive capacity. In contrast, areas of agriculture in the moist and lower threshold of the very moist precipitation groups show more consistent TNpp max values [Note: Comparisons between Ferric Luvisols and Chromic Luvisols are not included in this paper]. This suggests that when agricultural areas receive greater than ~1,200 mm of rainfall annually, these represent extreme sites for agriculture with a higher likelihood of uncertainty in yields.

Comparison of the dominant soil groups, mean annual precipitation, mean grow temperature, and Total Net Primary Productivity (tNpp)

An unsupervised cluster analysis identified three natural clusters of tNpp classified as (low < 5.12, medium > 5.12 & < 9.51, and high > 9.51 Mg ha -1 yr -1 ). An omnibus Welch’s heteroscedastic F Test for the tNpp clusters found significant differences among the six dominant UN FAO soil types in the low and medium tNpp groups, whereas post hoc Bonferroni correction for multiple pairwise comparisons resulted in no evidence of significant differences in the high tNpp group ( Table 2 ).

Soil groups with ( n ≥ 8). Letters indicate column-wise comparisons among the soil groups (where the mean of tNpp does not differ among soil groups displaying the same letter). Aggregated crop lands (bottom row).

https://doi.org/10.1371/journal.pstr.0000122.t002

A summary comparison between tNpp and the soil types identified Luvisols and Nitosol soil types as having the highest range of tNpp values (there was no low range of tNpp observations among Nitosol soil types) ( Table 2 ).

The lowest tNpp were recorded in Xerosols (tNpp range 0.0–6.4 Mg ha -1 yr -1 ), Vertisols (1.0–13.0 Mg ha -1 yr -1 ) and Cambisols (0.2–9.8 Mg ha -1 yr -1 ). The highest tNpp ranges also had higher eFit values and ranges: Fluvisols (0.9–4.7%), Luvisols (0.5–6.6%), and Nitosols (2.1–8.9%). The lowest eFit means were recorded in Xerosols, Vertisols, and Cambisols soil types (UN FAO classification). The variability of eFit was highest with Luvisols and Nitosols, indicating a wide range of productive capacity where there may be opportunities for increasing yields ( Table 2 ).

We also explored whether productivity varied significantly among the dominant soil types by fitting a linear model (estimated using OLS) to predict tNpp with soil group. The model explains a statistically significant and substantial portion of the variance ( R 2 = 0.38, F (5, 350) = 43.03, p < .001, adj. R 2 = 0.37). The model’s intercept, corresponding to Cambisols, is at 4.31 (95% CI [–3.64, 4.98], t (350) = 12.68, p < 0.001, AIC = 1844). To identify the influence of climatic variables to the response we then fitted a linear mixed model (estimated using REML and nloptwrap optimizer) with grow temperature and precipitation as fixed effects and dominant soil group as a random effect. The model’s total explanatory power was substantial R 2 = 0.47 with the fixed effects accounting for R 2 = 0.14. The model’s intercept corresponding to precipitation and grow temperature = 0 is at –12.06 (95% CI [–18.55, –5.58], t (351) = –3.66, p < .001). Within this model: the effect of temperature is statistically significant and positive (beta = 0.64, 95% CI [0.40, 0.88], t (351) = 5.21, p < .001, Std. beta = 0.21), the effect of precipitation is statistically significant and positive (beta = 2.06 −03 , 95% CI [1.38 −03 , 2.74 −03 ], t(346) = 5.97, p < .001, Std. beta = 0.28). The model was slightly improved (AIC = 1818).

A statistical analysis of the high and low TNpp max groups to the six dominant UN FAO soil types by one-way ANOVA found significant differences between at least two dominant soil groups ( Fig 6 ). The Welch’s F -test assumes that data groups are sampled from populations that follow a normal distribution but does not assume that those two populations have the same variance. Pairwise comparisons with correction for multiple comparisons indicated significant differences between varying combinations of all soil types. There were no significant differences in actual tNpp between Cambisols and Fluvisols, nor between Cambisols and Xerosols in either TNpp max group.

Comparison between (a) high TNpp max group (> 289.1 Mg ha -1 yr -1 ) and (b) low TNpp max group (≤ 289.1 Mg ha -1 yr -1 ) to the dominant UN FAO soil groups ( n > 8). Test results reported at top are for the omnibus heteroscedastic F Test with Bonferroni correction. Statistically significant pairwise comparisons are delineated by brackets with the p value reported above the line (* ≤.05, *** < .001). The large dot is the mean and is reported adjacent to the plot. The thick horizontal line is the median. The box in the center represents the interquartile range and the vertical thin gray lines above and below the box represent the rest of the distribution. The grey dashed horizontal line represents the grand mean. The totality of the curved shape represents a kernel density estimation where wider sections represent a higher probability of a given value, and thinner sections represent a lower probability.

https://doi.org/10.1371/journal.pstr.0000122.g006

The analysis identified statistical differences among varied combinations of soil types, where the effect size of the high TNpp max group was ( g = 0.82) ( Fig 6a ) and the effect size of the low TNpp max group was ( g = 0.86) ( Fig 6b ). These values are considered high per convention, indicating they are more probable under the alternative hypothesis.

We can infer that the overall relationship between soil types and tNpp are more strongly supported by the evidence than TNpp max . In this instance ( Fig 6 ), the effect size indicates that the low TNpp max group (panel b ) is more probable under the alternative hypothesis.

Similar to the TNpp max comparison to grow temperature ( Fig 4 ), a temperature threshold was found above 26°C when comparing tNpp to grow temperature ( Fig 7 ). In contrast to TNpp max , tNpp did not support a decrease in productivity as temperatures increased. In fact, tNpp was maintained and increased at temperatures higher than 27.5°C. In this comparison, crops growing on Vertisol soil type maintained a similar range of tNpp (5 to 10 Mg ha -1 yr -1 ) in the temperature range between 25.0 to 27.5°C. The highest tNpp levels were found in the Nitosol soil type, where tNpp exceeded 20 Mg ha -1 yr -1 ( Fig 7 ). Luvisols had the highest variation in tNpp compared to the other five dominant soil types (UN FAO).

A temperature threshold of 26° C is indicated by the dashed red line, and the Pearson’s Product Moment correlation ( r ) with p -value is reported at the top for each soil type.

https://doi.org/10.1371/journal.pstr.0000122.g007

In contrast to the relationship between TNpp max and mean annual precipitation ( Fig 5 ), there was no upper threshold of tNpp at 1,200 mm of mean annual precipitation. The precipitation threshold reached 1,600 mm ( Fig 8 ). Total net primary productivity varied over a range from 1 to 22 Mg ha -1 yr -1 in the Moist and Very Moist precipitation classes. This suggested that tNpp levels were not limited by precipitation levels. The highest tNpp levels were found in the Nitosols (6.0–25.4 Mg ha -1 yr -1 ) and Luvisols (1.4–21.1 Mg ha -1 yr -1 ) soil types (UN FAO), while the lowest range of tNpp values (1.0–13.0 Mg ha -1 yr -1 ) were produced in the Vertisols, Fluvisols (2.6–13.5 Mg ha -1 yr -1 ) and Cambisols (0.2–9.8 Mg ha -1 yr -1 ) soil types (UN FAO). Nitosols show optimal productivity with increasing temperature and precipitation conditions, whereas crops on Cambisols do not show higher productivity across the entire range of precipitation and temperature. Therefore, under limiting growth conditions Nitosol soils are still productive.

Four precipitation classes; dry, moist, very moist, and wet mean annual precipitation thresholds are from Vogt et al. [ 69 ]. The optimal range of precipitation for agricultural productivity is indicated by the grey panel (500–1200 mm -1 yr -1 ). Pearson’s Product Moment correlation ( r ) with p -value is reported at the top for each soil type.

https://doi.org/10.1371/journal.pstr.0000122.g008

The National Bureau of Soil Survey and the Land Use Planning under the control of the Indian Council of Agricultural Research (ICAR) have conducted extensive studies on Indian soils [ 70 ]. The ICAR classifies soils based on their characteristics as per the Soil Taxonomy of the United States Department of Agriculture (USDA). Chief characteristics are based on genesis, color, composition, and location. The three categories relevant to our reference sites were as follows:

(i) Alluvial soils, comprising ~43% of Indian soils dominate the northern plains and river valleys, and found extensively in deltas and estuaries. Alluvial soils are highly fertile, (ii) Red / Yellow soils are found largely in drier areas and are generally nutrient deficient with sandy to clayey and loamy texture, and (iii) Black soils are best for the cultivation of cotton and cover most of the Deccan plateau, which is characterized by deserts, xeric shrublands, and dry tropical forests. Although these areas receive less rainfall, Black soils have high water retaining capacity and are rich in minerals but deficient in N, P, and organic matter, with a clayey texture.

An unsupervised cluster analysis identified three natural clusters of tNpp among ICAR soil classifications as: (i) low, 0.0–5.1 Mg ha -1 yr -1 , (ii) medium, 5.2–9.5, and (iii) high, 9.7–25.4 Mg ha -1 yr -1 ( Table 3 ). The analysis found tNpp was significantly lower in the Alluvial soils (mean = 2.93, n = 50) compared to the Red / Yellow soil types (mean = 3.69, n = 26) in the low productivity group ( W Mann-Whitney = 996.00, p < 0.001). Whereas, these two groups were similar in the medium productivity group ( X 2 Kruskal-Wallis (2) = 8.88, p = 0.01), and high productivity group ( X 2 Kruskal-Wallis (2) = 8.47, p = 0.01). These comparisons did not identify the range of tNpp that resulted from using the dominant UN FAO soil types. Table 3 shows the clusters of tNpp (low, medium, high) ranked by the three major soil type groupings.

Omnibus tests by tNpp group indicated significantly higher tNpp in red/yellow soil types in the low tNpp group ( W Mann-Whitney = 996.00, p < 0.001), and differences of tNpp among the soil types of the medium tNpp group ( X 2 Kruskal-Wallis (2) = 8.88, p = 0.01), and the high tNpp group ( X 2 Kruskal-Wallis (2) = 8.47, p = 0.01).

https://doi.org/10.1371/journal.pstr.0000122.t003

These results indicate that a soil type may be more important at lower productivity levels. But, as the three India soil types include several UN FAO soil types, it was less successful in identifying low, medium, and high clusters. Each soil type was found in all clusters except for Black which is not present in the low tNpp cluster. This suggests that volcanic soils produce crops in the medium and high tNpp groupings, and these groupings were less able to identify productive agricultural fields that are using the UN FAO soil type produced ( Table 3 ). When comparing the Nitosols soil type (UN FAO) and the Black soil type, they had very similar medium and high tNpp clusters, but the other soil types did not produce similar tNpp clusters with the UN FAO soil types.

Comparison of the dominant soil groups, mean annual precipitation, mean grow temperature, and Ecosystem Fit (eFit)

All soil types had a large range of tNpp, suggesting no significant differences in eFit by dominant soil types ( Fig 3 ). Significant differences were found in eFit by the dominant soil types (UN FAO) grouped by the low and high TNpp groups ( Fig 9 ). Significantly higher eFits were found in the Nitosols soil type (UN FAO), suggesting that this soil type could potentially support higher tNpp through management. The second highest potential growth rates increases were found in the Luvisols soil type (UN FAO), also suggesting management can increase tNpp. All the dominant soil types in India had low eFits ( Fig 9 ) compared to the soils in Japan where eFit of 19% was reached, that was twice as high as eFits calculated for India (8.9%) (see Fig 2 ) [ 60 ]. The variability in eFit was the highest for Luvisols and Nitosols in India, suggesting these soil types have a greater potential for increased management to succeed in increasing growth rates ( Table 2 ).

Comparison between (a) high TNpp max group and (b) low TNpp max group to the dominant UN FAO soil groups ( n > 8). Test results reported at top for omnibus heteroscedastic F Test with Bonferroni correction (α = .05). Statistically significant pairwise comparisons are delineated by brackets with the p value reported above the line (* ≤.05, *** < .001). The large dot is the mean and is reported adjacent to the plot. The thick horizontal line is the median. The box in the center represents the interquartile range and the vertical thin gray lines above and below the box represent the rest of the distribution. The totality of the curved shape represents a kernel density estimation where wider sections represent a higher probability of a given value and thinner sections represent a lower probability. The grey dashed line represents the grand mean.

https://doi.org/10.1371/journal.pstr.0000122.g009

The TNpp max ( Fig 4 ) suggested a negative relationship between TNpp max and mean grow temperature; however, the opposite relationship was produced between Ecosystem fit and mean grow temperature ( Fig 10 ). A positive linear relationship suggests that as temperature increases, the eFit of a crop will increase due to reaching higher tNpp levels ( Fig 10 ). The significant 26°C threshold produced with TNpp max ( Fig 10 ), is not a threshold produced with eFit. The tNpp included in the eFit estimates show that the range of variance in eFit does not vary between 24°C and 29°C ( Fig 7 ). The dominant soil types support and maintain a range of eFit values ( Fig 10 ), with Luvisols and Nitosols consistently producing a higher eFit than other soil types. These results suggest that eFit is less impacted by temperature than by the dominant soil type.

An average growing season temperature threshold of 26° C is indicated by the dashed red line, and the Pearson’s Product Moment correlation ( r ) with p -value is reported at the top for each soil type.

https://doi.org/10.1371/journal.pstr.0000122.g010

In contrast to the relationship between TNpp max and mean annual precipitation ( Fig 5 ), eFit did not reach an upper threshold with mean annual precipitation at 1,200 mm ( Fig 11 ). For example, eFit varied across a precipitation range of 500 to 1,600 mm of mean annual precipitation while the thresholds of eFit were determined by the dominant soil types (UN FAO). The mean annual precipitation varied from the Moist and Very moist precipitation classes. This suggested that eFit levels were not limited by precipitation levels, and crop adaptation to its site will be less impacted by precipitation compared to other site factors. The highest eFits were found in the Nitosol and Luvisol soil types (UN FAO). The lowest eFit levels were found in the Vertisol, Fluvisols and Cambisols soil types (UN FAO).

https://doi.org/10.1371/journal.pstr.0000122.g011

Since climate change significantly impacts potential crop yields, it is important to develop a new framework to assess whether a crop plant can adapt to its site-level growth limiting factors. Today, the promise of transgenic hybrid traits and increased NUE of crop plants are not delivering the expected increases and have resulted in soil pollution and degraded soil health [ 71 ]. Since most plants do not reach their genetic productivity potential [ 58 ], it is worth exploring how well the light-use-efficiency model compares to the total net primary productivity focus to select crops to grow in diverse site conditions.

The study was designed to compare two assessment frameworks to identify which factors sensitively detect and identify the variables that determine the potential yields and productivity of a crop at the site level: (1) the light-use-efficiency model (carbon-centric model) to assessing yield potentials that included applying nitrogen fertilizers to increase a plant's leaf area (increasing yields focuses on managing a plant’s genotypic plasticity); and (2) the use of eFit based on tNpp (increasing productivity focuses on managing a plant’s phenotypic plasticity). The former framework focuses on photosynthetic efficiency while the second focuses on a site-scale index of a plant’s total productive capacity (i.e., biomass), not its yields, which varies under different soils and micro-climatic conditions [ 35 , 46 ].

Country-level (large-scale) and high-resolution (small-scale) localized data can be used to explore the utility of both frameworks since India experiences a high frequency of droughts that decrease crop yields. Goparaju and Ahmad [ 72 ] included a map of India showing the decadal (2005 to 2014) precipitation deficit, suggesting that the entire country experiences droughts that will impact crop growth and yields. The extensive areal coverage of droughts in India indicates the importance of having a framework that allows crops to be selected based on the diversity of soil and climatic conditions found at the site level. Since droughts in India occur throughout most of the country, there is a need to be able to select crops that are fine-tuned to India’s agricultural landscape at the plot level. Plants also need to adapt and grow under stochastic drought conditions in a diversity of edaphic and climatic conditions. When a farmer cannot manage their crops by irrigation and fertilizer applications, they need to be able to select crop plants capable of growing in their specific soil and micro-climate and capable of adapting to short-term changes in edaphic conditions.

Here, the variation in the TNpp max based on the light-use-efficiency model did not explain the significant differences in tNpp of crops on the dominant soil types (UN FAO), it suggests that a light-use-efficiency model is not sensitive to measuring changes in crop yields at the small-to-meso scale. This research used TNpp max , a surrogate to the light-use-efficiency model, across the different ecological and soil zones of India’s major commercial crop regions and found no significant relationships between TNpp max and the dominant soil types as well as temperature and precipitation. This model is not sensitive to identifying how a crop adapts to its changing environment mechanistically [ 73 ]. Also, it does not factor in a crop’s ecophysiological and evolutionary adaptations at the site scale and could not identify climatic conditions as setting growth thresholds [ 46 ].

Photosynthetic rates of a plant have been essential variables to monitor to determine the amount of carbon a plant can fix and how much biomass is produced based on the limits to growth at the site level. Despite multispectral images being used to predict the yields of Zea mays to assess when crops need to be irrigated, nutrients applied, and when insects or disease organisms attack them [ 74 ], it does not allow you to determine if a plant can still adapt to the changing environmental conditions occurring with climate variability. Multispectral images work as part of management because they focus on changes occurring at the leaf level but not on the plant holistically as an organism responding to its environment. This means that increasing the photosynthetic efficiency is important, but relying on it exclusively would ignore how the site factors limit the ability of a plant to increase growth.

The second approach is eFit, i.e., which is based on tNpp and focuses on plant phenotypic plasticity. This model focuses on the impact of site edaphic and micro-climatic conditions on crop growth and selecting plants better adapted to local site growth conditions. This framework was sensitive to the dominant UN FAO soil types and identified three soils types (Cambisols, Luvisols, and Nitosols) that had a significantly higher upper threshold of tNpp of 19.9, 21.3, and 25.4 Mg ha -1 yr -1 , respectively. This suggests that these soil types support high growth rates. Except for Nitosols, some sites were also in the low and medium tNpp clusters, suggesting that these sites have other site-limiting growth factors that reduce the potential achievable productivity at these sites. Interestingly, each dominant soil type was represented in the three tNpp clusters except for the Nitosols and the Xerosols. It would be worth focusing on each of the tNpp clusters and researching each site to determine what factors placed some sites in the low and medium cluster groups.

The highest tNpp levels were recorded in the Nitosol soil type, which is also expected since these are well-drained, deep soils with a clayey subsurface horizon [ 75 ]. Nitosols are also soils where deep-rooting crops should be planted so crops can access deeper sources of water and nutrients, allowing higher resilience under drought conditions. Other soil types had lower or intermediate tNpp levels, suggesting that management could mitigate multiple site-level factors that limit growth. For example, Xerosols were devoid of crops growing in the high tNpp cluster, which is expected considering these are desert soils with low organic matter and N levels and need to be irrigated to be productive [ 75 ]. Also, Fluvisols and Vertisols had significantly lower tNpp; Fluvisols are very young soils and need to be irrigated to grow crops, whereas Vertisols shrink and swell depending on moisture levels. Further assessment of the low, medium, and high tNpp clusters is worthwhile pursuing to determine why there are statistically significant clusters of low productivity. This could help managers determine what crops to grow on their land based on factors limiting growth.

The high frequency of drought helps to explain the low eFit values by dominant soil types (UN FAO) in croplands. Compared to the eFit values of 14% for rice and 19% for maize calculated by Gordon et al. [ 60 ], the values for India varied from 0.3 to 8.9%, suggesting that some soil types produced higher biomass, but there are many sites at the lower end of eFit percentages. The large variation in eFit by soil type also shows that other factors limit the growth of crops that need to be evaluated. The highest tNpp and eFit were recorded in UN FAO’s Nitosols soil type. This justifies increasing management practices to focus on ameliorating poor soil health and planting crops with sufficient allocation to roots to improve crop yields.

We identified areas that need further research to determine whether the underlying soil factors can be managed to improve the potential productivity of agricultural lands. Further exploration of the relationships between soils and productivity would need to use the soil types (UN FAO classification scheme) to explore how to increase the productive capacity of these sites. The three dominant agricultural soil types (Alluvial, Red/Yellow, and Black soil types also used by the Indian Council of Agricultural Research) [ 70 ] were not sensitive in identifying the ranges of tNpp in each soil type due to their generality and aggregated nature of encompassing several UN FAO soil types. This made them less useful for selecting plants to grow at a site, especially since similar crop plants were grown in each soil type [ 70 ].

This research developed a framework that can be an early warning indicator that plant growth rates may decrease due to the most limiting resources, e.g., rainfall. In contrast, the photosynthetic-use-efficiency model did not indicate the source-sink relationships and, thus, how a plant phenotypically adapts to its environment by shifting allocation between defense, nutrient uptake, and fixing carbon [ 46 ]. This is where determining the eFit [ 63 ] shows promise as a framework to measure the growth yields at the site-specific scale. It will assess how much the site edaphic and micro-climatic conditions limit a plant from reaching its productive capacity, i.e., how close a plant can grow to its theoretical maximum potential productivity based on the growth limiting resources. Gordon [ 60 ] wrote about the need to manage where you are to achieve ecosystem management at the site level. Sun et al. [ 76 ] reported that an integrated measure of soil and leaf physiological factors was most indicative of crop yields. Also, they reported that soil organic matter levels and metabolic enzymes, e.g., invertase, sucrose synthase, were the dominant factors that affected the yields of banana plants. This work supports the need for a more integrative approach to assess the limits to crop yields and why developing an eFit and crop productive capacity at the site level is warranted and needed.

In some situations, with interest in developing sustainable agricultural practices, alternative approaches that ameliorate the soil organic matter levels or interplanting trees/shrubs with crop plants will need to be explored [ 77 – 79 ]. This would approach agriculture from the angle of remediation of the edaphic environment to increase its retention or water-holding capacity when climate change results in decreased precipitation levels, as shown by Goparaju and Ahmad [ 72 ] for India’s major grain production areas. They called for a diversified approach to address climate change impacts and better-diversified farm output [ 80 ]. These are important factors that need to be addressed. Still, we would suggest that there needs to be a better approach to selecting plants to grow in different parts of India (and other places around the world), considering that drought frequencies are high. A high proportion of India’s agriculture experiences droughts as shown by Goparaju and Ahmad [ 72 ], with 54% of India’s total land area experiencing high or extremely high-water stress.

A future experiment is needed that combines the first phase of this research as tools to see ‘how plants are doing’ and then measure the productive capacity of a crop planted across the latitudinal and ecoregions of India. These data could then be used to develop a framework that could inform a decision tree to determine what plants to grow in different soils in India. It would combine phenotypic and genotypic factors to select plants for different sites and determine an eFit for each crop. This is especially important with the climate change impacts we are experiencing since the traditional approaches to selecting and managing crops may be less suitable and less flexible. Today, the climatic conditions are different, and their impacts vary based on the edaphic conditions of a local site.

Since most plants do not reach their genetic growth potential, a holistic approach is needed to assess plant growth potential to identify site-scale growth-limiting factors. This would include a plant’s photosynthetic potential but should also include improved adaptation at the root level to select more suitable crops to grow [ 45 ]. This is based on how site-level edaphic and climatic factors constrain a plant’s productive capacity. Suppose these crop growth limiting factors cannot be alleviated, e.g., soils with low water holding capacity or low nutrients [ 69 ], and thus have a lower potential to achieve higher total yields. In that case, lower crop yields are possible, and crops that are better adapted to the soil and climatic conditions at the site scale should be selected for cultivation. This is because plants allocate carbon to tissues and organs that acquire the scarce resources needed for its growth. When this does not happen, crop yields of the product being grown may decrease, as a plant may allocate more to roots to acquire the nutrients deficient in the soil or grow deeper roots during drought conditions.

Understanding how plants adapt to a dynamic climate is required in order to compensate for site constraints that modulate growth rates. This would require developing a unique site-specific internal reference that combines knowledge of the plant growth rates and their adaptive capacity to dynamic growth environments. The goal is to achieve higher growth rates at the site by guiding the allocation of energy to the desired plant parts. The internal reference of productivity potential represents the energy available for the response of phenotypic adaptation to a diversity of soil and climatic conditions and how each plant’s adaptive capacity interacts with its growth environment [ 35 , 69 ].

Intensive farm management practices will continue to be utilized since larger-sized farms (> 50 ha in size) accounted for more than 70% of the world’s farmland area in 2010 [ 81 ]. The problem with larger farm sizes growing the same crop is that planting genetically similar or identical varieties of crop plants means that it will increase the area of crop growth but does not allow for plants to phenotypically adapt to the wider range of soil conditions that it will experience under the stochastic changes in growth conditions. If the average size of a farm continues to increase globally, technological developments will be essential to efficiently and economically manage and harvest farm fields due to farm labor shortages [ 82 ]. This has driven the focus on increasing crop yields by increasing photosynthetic efficiency [ 83 – 84 ]. The source-sink relationships, however, suggest that as a crop genotype selected mainly for an increased photosynthetic efficiency to increase crop yields might not be adapted to mitigate future resource-limiting factors, such as water or nutrients, as those selected genotypes may be less able to allocate carbon for increased root productivity [ 35 – 36 ]. Research by Banerjee [ 85 ] found that foliar plasticity is not part of the adaptive capacity of a crop to its environment and that genetically selecting a crop for its greater drought tolerance reduced the biomass growth of Phaseolus vulgaris L. As more carbon is shifted to a plant component, allowing it to acquire one of the limiting growth resources, there then becomes insufficient carbon to allocate to other plant parts that enable a response to other growth limiting factors, i.e., evolutionary tradeoffs.

The caveat of evolutionary tradeoffs is that there is no increase of functionality, nor increase of energetic efficiency of one part of a biological system that does not require compensation of another, i.e., there are inherent mechanisms that manage the process. Tradeoffs manifest at every level of biological organization (i.e., cellular to organismal to ecological) and arise because individual traits that we wish to promote in crops are imbedded within complex integrated systems of traits that make up whole organisms.

In terms of evolutionary biology, tradeoffs are the process through which a trait increases the fitness of an organism. Tradeoffs are integral to life because there are always competing demands for limited resources that drive the response to constraints, and this process is why organisms optimize their adaptive ecophysiology. Tradeoffs among crop plants generally can be defined in several non-mutually exclusive terms [ 86 ]:

• allocation constraints caused by a limited resource, such as increasing allocation to roots instead of leaves under drought conditions,
• functional conflicts, where an enhanced performance of traits for higher yields decreases nutritional value, tolerance of high temperatures, or resistance to pests;
• shared biochemical pathways arise from highly conserved molecular pathways that are shared between different traits, some that benefit fitness (e.g., survival, reproduction, fecundity) and some that are detrimental to fitness;
• antagonistic pleiotropy, where one gene controls more than one trait. For example, a gene is selected as it is beneficial for reproduction in early life stages, but also codes for accelerated aging, co-selecting for senescence;
• growth-defense/ecological interactions, herbivory triggering an increased production of secondary metabolites or immobilization of sugars to dissuade pests, thereby taking resources from reproduction or growth. Also, every plant−pathogen-pest system is unique, and management will need to understand the tradeoffs with a particular crop or field condition; and,
• abiotic stressors, such as conservative stomatal behavior in terms of water loss per unit carbon gain under warming and drought conditions. We should expect complex linkages between different tradeoffs, e.g., root hydraulics/root length affecting stomatal physiological and leaf cooling mechanisms linked to aerodynamic leaf resistance and evaporative cooling.

Tradeoffs are most common when energetic or nutrient challenges are extreme. The plant types that persist in harsh conditions are usually found in the tail ends of phenotypic distributions. Tradeoffs are also embedded within a temporal framework where the compensatory mechanisms may operate over very different time scales; from immediate to evolutionary.

Crop plants respond to changing climatic regimes and variable site conditions via physiological changes that enable their localized adaptation. All responses draw from available energy and nutrient pools, so trade-offs must be prioritized. Crops will need adaptive flexibility as the demand and complexity of these switches increase; this will influence crop success (however it is measured) in responding to variability in environmental conditions. Consideration must also account for soil-microbial-mycorrhizal-plant interactions and the tradeoffs required to maintain symbiotic associations. All crops cope with pests, pathogens, and physical damage from weather, including wind, drought, flooding, and temperature extremes that occur outside the safe operating space for a species or variety. Crops inevitably make “decisions” about when, where, and how to allocate their available resources, ultimately determining yields.

The existing paradigm must be overhauled when farming strategies or management cannot produce reliable crops year-over-year. Climate variability will require flexibility in the selection, cultivation, and expectation of plant performance, which realistically accounts for all the variables impacting yields. As we cope with our changing environment and work to foster and utilize a plant’s innate adaptive capacity to respond to increasingly dynamic stressors, an approach that balances available resources will fully benefit from plant genetic diversity. Farmers need to select crop plants capable of growing in specific soil and micro-climate niches and that also possess the traits to adapt to short-term changes in environmental conditions. Plants that are specialists in adapting to site-level conditions must also be generalists adapting to unpredictable temperature and precipitation regimes. A sort of “surgical precision” farming that can respond to small-scale characteristics across the “grow landscape” that may change every season and can produce sufficient and reliable yields to warrant the effort.

Trade-offs will be a necessity along with compromises that may result in moderate returns that take time to build to a better-than-average yield, i.e., avoiding boom and bust cycles associated with drought with conventional crop breeds or genetically modified varieties. This will re-establish crop resilience and reliability and regain the knowledge of experience lost over the last one hundred years since the green revolution and the advent of industrial-scale mono-crop farming. There is a new urgency to understand how best to exploit the adaptive traits heritage varieties possess while also increasing the diversity of crops, identifying new beneficial traits, and re-establishing lost or endangered strains adapted to niche characteristics of the agricultural diversity of India.

Understanding how crops will respond to changing climatic conditions in all dimensions is the core of agroecology. In the face of rapid environmental change natural populations avoid extinction via two evolutionary mechanisms; phenotypic plasticity and/or adaptive evolution [ 87 ]. How the interplay between these evolutionary processes and changing environmental selective pressures will unfold remains less clear. We need to better understand the levels at which existing crop genetic diversity and phenotypic plasticity help or hinder adaptive capacity at the site level, and our study provides a promising path to explore this avenue, as we search for ways to incorporate sustainable crop management while still meeting our food and energy demands.

Materials and Methods

Site description and droughts.

India was selected as the case study because it’s where the original Green Revolution emerged to address inadequate agricultural productivity to feed the country’s rapidly growing population. India is an Agrarian society and emerged as the most populous nation in April 2023 [ 88 ]. India also has a rich history of research and data on its soil types and climate and is experiencing a loss of crops due to droughts [ 72 ]. India has experienced many droughts and an increasing frequency of significant droughts over the last 20 years compared to an average year [ 89 ]. A report on the meteorological history of droughts recorded the following pattern [ 89 – 90 ]:

• During 1871–2015 , there were 25 major drought years , defined as years with All India Summer Monsoon Rainfall (AISMR) less than one standard deviation below the mean (i . e ., anomaly below percent) : 1873 , 1877 , 1899 , 1901 , 1904 , 1905 , 1911 , 1918 , 1920 , 194 1 , 1951 , 1965 , 1966 , 1968 , 1974 , 1979 , 1982 , 1985 , 1986 , 1987 , 2002 , 2009 , 2014 and 2015 .
• The frequency of drought has varied over the decades . From 1899 to 1920 , there were seven drought years . The incidence of drought came down between 1941 and 1965 when the country witnessed just three drought years .
• However , during the 21 years , between 1965 and 1987 , there were 10 drought years which was attributed to the El Nino Southern Oscillation (ENSO) . Among the many drought events since Independence , the one in 1987 was one of the worst , with an overall rainfall deficiency of 19% which affected 59–60% of the normal cropped area and a population of 285 million . This was repeated in 2002 when the overall rainfall deficiency country as a whole was 19% .

These droughts are reducing crop yields but at different rates across its agricultural landscapes. India’s agricultural areas experienced either low or high drought frequency between 2000 and 2019 [ 89 ]. Agricultural productivity decreased by 40% where farming is dependent on precipitation. The worst drought year in India was 1987, when rainfall was 75% below normal. Kumar [ 91 ] reported rainfall was less than 50% of the average, and food grain output productivity of the yield dropped by 20%. Also, Kumar [ 91 ] reported that between 1978 and 1983, lands that were not irrigated had a 30–50% decline in yields, but even irrigated lands experienced decreased yields of 10–20%. The food grain productivity decreased by 29 million tons from an expected 90 million metric tons for this year [ 91 ].

Data, study design and analysis

This paper used 356 country-wide sites with administrative-level climatic and soil data to demonstrate eFit and tNpp’s utility in developing an internal site standard. We filtered the data to the land cover classification of croplands west of 90° E longitude, with a grow season temperature above 19° C and a sample size greater than eight ( n > 8) in each dominant soil type. We explored how much growth-limiting factors decrease tNpp, identified the potential productivity at the site level, and the potential for managers to increase actual productivity.

This study was designed to compare two assessment frameworks to identify sensitivity to site-level factors and which variables determine the potential yields and productivity of a crop at a site level: (1) the light-use-efficiency model (or carbon-centric approach); and (2) the use of eFit based on total tNpp. The second framework incorporates the first framework and expands the assessment to be sensitive to site-level limitations on crop growth by eco-region and dominant soil types in India. The framework aims to estimate a crop’s TNpp max and actual productive capacity based on local edaphic and climatic conditions under which plants actually grow.

This research explored whether a plant’s photosynthetic potential will be sufficient to determine its productive potential and whether it can link the site-level limits of soils and climates. This framework uses ‘total’ crop productivity, not just the ‘product’ biomass harvested. It provides methods to estimate the maximum photosynthetic potential of a site and the actual productivity to determine which cultivars should be planted at a given site, especially focusing on the soil and climatic characteristics by knowing how the crop may adapt to its site.

Within a GIS we mapped the primary boundary of India to constrain the spatial extent of our analysis ( https://map.igismap.com/gis-data/india/administrative_outline_boundary ) and level-two administrative divisions (which includes districts and is part of the Global Administrative Areas 2015 (v2.8) dataset). Reference sites consisting of spatial datapoints were developed by calculating the centroid of each second-level administrative boundary division, including districts, that is part of the Global Administrative Areas 2015 (v2.8) dataset ( https://gadm.org/ ).

Prior to further analysis, the northeast region of India consisting of the eight states of Assam, Arunachal Pradesh, Manipur, Meghalaya, Mizoram, Nagaland, Tripura, and Sikkim were removed from the data set as the landscape is dominated by tropical, subtropical, and temperate broadleaf and mixed forests where there is limited agricultural development. Through a series of steps within a geographic information system (GIS), a diversity of data types and sources were gathered and assigned to each site with overlay and spatial relationship functions.

Environmental data included ecoregion classification from the Terrestrial Ecoregion GIS data portal ( https://www.gislounge.com/terrestrial-ecoregions-gis-data/ ) which depicts 846 described ecoregions across the planet. Ecoregions are ecosystems of regional extent, color-coded to highlight their distribution and the biological diversity they represent with the goal of E. O. Wilson’s “Nature Needs Half” initiative to protect half of all the land on Earth to save a living terrestrial biosphere ( https://ecoregions.appspot.com/ ).

Major soil types were downloaded from the FAO soil survey portal ( https://www.fao.org/soils-portal/soil-survey/ ), and land cover classifications, and other climatic and environmental variables consisting of vector and raster data were then spatially joined or summarized to the reference sites for further processing [ 92 ]. We included only sites where land cover was classified as “cropland,” which served as the locations to calculate TNpp max , total productivity, and eFit estimates.

Digital Soil Map for India

India is a very diverse landscape composed of 39 level IV ecoregions (21 used in this study) and 10 biomes (5 used in this study). Six dominant soil types were associated with the reference sites, and an additional ten soil types represented the total diversity of soil orders found in India ( Fig 12 ).

Country-wide cropland reference sites (dark points) based on the centroid of administrative units (n = 356). Level IV ecoregions are encompassed within the biomes outlined in yellow and listed by number on the map and described in the legend. The colors indicate the dominant soil types used in this study, and the additional diversity of soil types not covered by a reference site are depicted as dark grey. Data sources for the base map as follows: Boundary of India layer is part of the Global Administrative Areas 2015 (v2.8) dataset. Hijmans, R. and University of California, Berkeley, Museum of Vertebrate Zoology. (2015). Boundary, India, 2015. UC Berkeley, Museum of Vertebrate Zoology. Available at: http://purl.stanford.edu/jm149wc6691 ; Soil vector data based on the FAO-UNESCO Soil Map of the World available at https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/446ed430-8383-11db-b9b2-000d939bc5d8 ; Ecoregion layer is licensed under CC-BY-4.0 and available at Ecoregions 2017 © Resolve https://ecoregions.appspot.com ; Administrative boundaries are derived from OpenStreetMap data licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) and available at GIS Data | MAPOG. There were no changes made to the base layers.

https://doi.org/10.1371/journal.pstr.0000122.g012

The characteristics of the three dominant soil types used in this study from the ICAR classification scheme are described in Table 4 .

https://doi.org/10.1371/journal.pstr.0000122.t004

Climatic Variables

The climatic variables were annual mean, minimum and maximum air temperatures, and total yearly precipitation as continuous data to tease out critical environmental thresholds influencing the growth of crops. In addition, average monthly temperature data and average monthly solar radiation data [ 93 ] were downloaded and extracted at the geographic coordinates of each reference site [ https://www.worldclim.org/data/bioclim.html ] at a spatial resolution of 30 seconds (~1 km 2 ). The summary data calculated for each site were: (i) the length of the growing season (i.e., days when the temperature exceeded zero), and (ii) the mean monthly temperature days for the growing season.

We evaluated the underlying structure of TNpp max and grow temperature with unsupervised clustering analysis to identify threshold effects. We computed all pairwise dissimilarities (distances) between observations based on a Euclidian metric with determination of the medoid by a robust partitioning method (PAM). The optimal number of clusters was selected by evaluating the average silhouette width and the variance explained by the first two principal components with the R package “cluster”. For the data analyses, precipitation thresholds were grouped into four classification levels described in Vogt et al. [ 69 ].

Calculation of Maximum Potential Productivity

To estimate crops’ maximum potential productivity (TNpp max ), a modified Loomis-Williams model initially developed for crops in the 1960s was used [ 61 – 62 ]. Their research supports using photosynthesis as each plant’s assimilation framework and explores site limitations on plant productive capacity [ 61 , 63 ]. A crop’s photosynthetic capacity is limited by the resources constraining its ability to fix carbon, such as its ability to acquire growth-limiting resources at the site level, e.g., its edaphic and micro-climatic conditions [ 69 ].

The specific theoretical maximum productive capacity potential was estimated using a light-use efficiency model, based on the amount of solar radiation available during a growing season at each site and its plant physiological parameters [ 35 , 93 ]. The light-use-efficiency model is calculated as a product of solar radiation, light-interception efficiency ( Ε i ), the efficiency at which canopies absorb solar radiation, and the conversion efficiency ( Ε c ), or the rate at which solar radiation is absorbed by C3 plants and is converted into biomass [ 35 , 62 , 94 ].

Calculation of Ecosystem Fit

Ecosystem Fit (eFit) is the proportion of productive capacity of the site that can be improved upon using as an index the upper site-level maximum threshold of total net primary productivity capacity for the site based on the site’s limitations to growth [ 63 ]. In this calculation, Net Primary Productivity (tNpp) integrates green plant functions and includes changes in carbon allocation shifts in the source-sink relationships [ 46 ].

The eFit model was developed for forests. It produces an internal site reference productivity level or an index of the site-level potential productive capacity to a plant’s ecophysiological and evolutionary functions, site-level edaphic conditions, and climatic constraints [ 62 – 63 ]. This indexing approach has not been previously used in agriculture to assess the eFit of a crop to local site growth-limiting conditions. Ecosystem fit cannot be currently calculated using field-collected data for crops in this study because of the lack of a robust database populated by data on a diversity of crop source-sink relationships to seeds, fruit, aboveground plant growth, root biomass, and secondary defensive chemicals. The methodology to calculate eFit is described in Klock et al. [ 62 ].

Statistical methods and calculations

To understand the underlying structure and threshold effects of the data, we applied an unsupervised (all identifying classifiers removed) clustering method to TNpp max , tNpp, and the climatic variables of temperature and precipitation. Clustering reveals the natural groupings inherent in the data and provides an empirical estimation of the thresholds that inform how to characterize the vegetation response across the landscape.

We hypothesized that soil conditions would inform primary productivity. Therefore, we tested for a threshold effect by computing all pairwise dissimilarities (distances) between observations based on a Euclidian metric with determining the medoid by a robust partitioning and aggregation around medoids (PAM). The medoid represents the stabilized median of the clustered data. The optimal number of clusters was selected by evaluating the average silhouette width and the variance explained by the first two principal components with the R package “cluster” [ 95 ]. The cluster analysis identified two relatively homogenous clusters for TNpp max and three clusters for tNpp, thus data summaries were classified by these groups, referred to as low, medium, or high (Tables 2 and 3 ).

A series of parametric and non-parametric approaches were explored to understand the association between productivity metrics (e.g., TNpp max , tNpp, eFit), and dominant soil groups and climatic variables. All continuous responses were evaluated for meeting the assumptions of normality (if applicable), and to reduce experiment-wise error rates (Type I error, falsely rejecting the NULL), we adopted a more stringent level of significance for all pairwise comparisons with Bonferroni correction to further control for the probability of committing a Type I error [ 96 ]. We tested for homogeneity of variance with a Bartlett test. The responses of TNpp max , tNpp, and eFit were normally distributed and mostly balanced (assumption of Gaussian distribution) and we used Welch F tests to evaluate the relationships to the dominant soil groups. The Welch test is based on the weighted means of each group (sample size and variance) and the grand mean based on the weights mean of each group for the sum of squares. These tests provided the general benefit of high power and low probability of Type I error. We report the grand mean value in figures to represent that value against which the class means were evaluated. We used partial Omega squared as a measure of effect size as it is widely viewed as a lesser biased alternative when sample sizes are small (e.g., a partial effect size [ω2 = 0.25] would indicate that 25% of the variance was explained by the predictor). We used the standardized effect size to represent the practical significance of our results, instead of relying on statistical thresholds, and to make comparisons among very different responses. We also tested for statistical power of each comparison. All statistical tests were based on (α = .05), with analysis conducted in the programming language R ver. 4.2.2 [ 97 ] and geospatial data processed using ESRI ArcDesktop ver 10.8 [ 98 ].

Supporting information

S1 file. outline of the framework for data processing with chunks of embedded r code..

https://doi.org/10.1371/journal.pstr.0000122.s001

S1 Data. Processed dataset for analysis.

https://doi.org/10.1371/journal.pstr.0000122.s002

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https://environmentagency.blog.gov.uk/2024/09/16/getting-winter-ready-a-case-study/

Getting winter ready: a case study

We recently met up with Tom, a dairy farmer in Cornwall, who had made some major changes to his farm infrastructure . Tom acted to bring his slurry storage into compliance with the Silage, Slurry and Fuel Oil r egulations and to help meet the requirements for Farming Rules for Water. This meant that he had enough storage to see him through winter and could comply with the requirements for spreading manures and fertiliser s to meet soil and crop need only.   

Tom’s slurry situation  

Tom found himself in a situation many farmers will be familiar with. Too much slurry and too little storage. Ultimately, what this ends up with is having to venture out and spread slurry on fields when it is not required by soil or crop. In general terms, there is no soil or crop need for nutrients during the autumn and winter months (though there are some exceptions). There are a lot of issues with spreading at this time of year:  

• It’s not compliant with Farming Rules for Water regulations on soil and crop need  
• Higher risk of pollution from runoff and soil erosion due to poor weather – minimising risk of pollution is also a requirement of Farming Rules for Water  
• Potential damage to soil – through compaction from heavy machinery; this in turn heightens the risk of pollution from run off and, crucially, diminishes soil health  
• Waste of nutrients – as Tom notes in the video, he grew a lot more grass by applying fertiliser during the growing season and not wasting it during the winter months   

What Tom did   

Tom took a range of different measures but for many farmers, they may not all be necessary.   

We would encourage all farmers to look at the more straightforward (relatively speaking) steps first – specifically for clean and contaminated water separation, which you can find out more about here . Understanding how much rainfall you receive and rectifying issues with drainage and guttering can go a long way to reducing the volume of slurry you collect. As Tom notes, he was able to roof existing buildings with grant funding, meaning a lot of improvements made for little or no outlay, and a great deal of benefit to his storage capacity.   

By working with us and getting his own independent advice, Tom has built a slurry lagoon that gives him six months storage for 500 cows. This is a significant buffer for his current requirements and assurance that he won’t have to revisit slurry storage on his farm for many years to come. Tom’s slurry lagoon was a more significant financial investment but he is now reaping the benefits by buying in much less bagged fertiliser. As he mentions in the video, he is growing a lot more grass by applying slurry to his land at the right time.  

Finally, he isn’t having to worry about his storage filling up and needing to spread when he shouldn’t. This gives peace of mind that he is compliant with Farming Rules for Water and the Silage, Slurry and Fuel Oil regulations.   

How to get your farm Winter Ready  

There is help available if you need to improve your slurry storage capacity. At the Environment Agency, we will always be happy to discuss your situation. We appreciate that, as a regulator, you may be hesitant about speaking to us, but if you contact us our first step will be to offer advice and guidance. We will work with you to make sure your plans are compliant and direct you to other sources of guidance and information.    

Catchment Sensitive Farming provide advice on possible sources of funding for bigger changes to farm infrastructure and what changes are likely to have the biggest impact for your farm. The Farming Advice Service also provide free technical and business advice on regulatory compliance.    

With thanks to Tom and our officer Rob for their time and effort in making this video.   

Tags: #WinterReady , slurry

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Organic Chemistry Frontiers

18f-labelling of nitrogen-containing aryl boronates − anti-cancer drug melflufen as a case study.

18F-Labelling of nitrogen-containing arenes via copper mediated radiofluorination (CMRF) was investigated. The studies targeted analogues of anti-cancer drug melflufen with an alkylating bis(2-chloroethyl)amino pharmacophore. Studies of melflufene anologues and various model compounds indicated that the copper mediated boron fluorine-18 exchange reaction is affected differently by the three nitrogen-containing groups in the target compound. The largest inhibitory effects for the fluorine labelling process was excerted by the tertiary amine based bis(2-chloroethyl)amino pharmacophore. The best results were achieved by applying bipyridyl ligands for the copper mediator.

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K. Bajerke, F. Lehmann, G. Antoni and K. J. Szabo, Org. Chem. Front. , 2024, Accepted Manuscript , DOI: 10.1039/D4QO01594K

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