Which Of These Is Not A Way In Which A Gm Animals Contribute To An Increased Food Supply?
PLoS One. 2013; eight(6): e64879.
Genetically Modified Crops and Food Security
Matin Qaim
1 Section of Agricultural Economic science and Rural Evolution, Georg-August-Academy of Goettingen, Goettingen, Deutschland
Shahzad Kouser
ane Department of Agricultural Economics and Rural Development, Georg-Baronial-University of Goettingen, Goettingen, Frg
2 Institute of Agricultural and Resource Economics, Academy of Agriculture, Faisalabad, Islamic republic of pakistan
Grand. Lucrecia Alvarez, Editor
Received 2013 Feb 20; Accepted 2013 April 18.
Abstract
The role of genetically modified (GM) crops for food security is the bailiwick of public controversy. GM crops could contribute to food production increases and higher food availability. There may besides exist impacts on food quality and nutrient composition. Finally, growing GM crops may influence farmers' income and thus their economic access to nutrient. Smallholder farmers make upwards a big proportion of the undernourished people worldwide. Our study focuses on this latter aspect and provides the first ex post analysis of food security impacts of GM crops at the micro level. We utilize comprehensive panel data collected over several years from subcontract households in Republic of india, where insect-resistant GM cotton wool has been widely adopted. Decision-making for other factors, the adoption of GM cotton has significantly improved calorie consumption and dietary quality, resulting from increased family incomes. This technology has reduced food insecurity by xv–twenty% among cotton-producing households. GM crops lonely will non solve the hunger problem, but they can be an important component in a broader nutrient security strategy.
Introduction
Food security exists when all people have concrete and economical admission to sufficient, safe, and nutritious food. Unfortunately, nutrient security does not exist for a significant proportion of the world population. Around 900 million people are undernourished, meaning that they are undersupplied with calories [ane]. Many more suffer from specific nutritional deficiencies, often related to bereft intake of micronutrients. Eradicating hunger is a central role of the Un' Millennium Development Goals [2]. Simply how to achieve this goal is debated controversially. Genetically modified (GM) crops are sometimes mentioned in this connectedness. Some meet the development and utilise of GM crops as central to reduce hunger [3], [4], while others consider this technology as a farther run a risk to food security [5], [6]. Solid empirical testify to support either of these views is sparse.
In that location are iii possible pathways how GM crops could affect food security. Offset, GM crops could contribute to food production increases and thus improve the availability of nutrient at global and local levels. Second, GM crops could affect food safety and nutrient quality. Third, GM crops could influence the economic and social situation of farmers, thus improving or worsening their economic access to food. This latter aspect is of particular importance given that an estimated 50% of all undernourished people worldwide are small-scale farmers in developing countries [7].
In regard to the first pathway, GM technologies could make food crops college yielding and more robust to biotic and abiotic stresses [8], [9]. This could stabilize and increase food supplies, which is important confronting the background of increasing food demand, climatic change, and land and water scarcity. In 2012, 170 one thousand thousand hectares (ha) – effectually 12% of the global arable country – were planted with GM crops, such as soybean, corn, cotton, and canola [x], merely almost of these crops were non grown primarily for directly food use. While agricultural article prices would be college without the productivity gains from GM applied science [11], impacts on food availability could be bigger if more GM food crops were commercialized. Lack of public acceptance is one of the main reasons why this has not yet happened more than widely [12].
Concerning the second pathway, crops with new traits can be associated with food safety risks, which have to exist assessed and managed case by case. But such risks are not specific to GM crops. Long-term enquiry confirms that GM applied science is not per se more risky than conventional plant breeding technologies [13]. On the other hand, GM applied science can help to breed food crops with higher contents of micronutrients; a case in point is Golden Rice with provitamin A in the grain [xiv]. Such GM crops have not yet been commercialized. Projections evidence that they could reduce nutritional deficiencies among the poor, entailing sizeable positive wellness furnishings [15], [16].
The 3rd pathway relates to GM ingather use past smallholder farmers in developing countries. Half of the global GM crop area is located in developing countries, but much of this refers to large farms in countries of South America. Ane notable exception is Bacillus thuringiensis (Bt) cotton wool, which is grown by around 15 meg smallholders in India, Red china, Islamic republic of pakistan, and a few other developing countries [ten]. Bt cotton fiber provides resistance to important insect pests, especially cotton bollworms. Several studies have shown that Bt cotton adoption reduces chemical pesticide apply and increases yields in farmers' fields [17]–[twenty]. At that place are also a few studies that have shown that these benefits are associated with increases in farm household income and living standard [21]–[23]. Higher incomes are generally expected to cause increases in nutrient consumption in poor farm households. On the other hand, cotton is a not-food greenbacks ingather, and then that the nutrition affect is uncertain.
Hither we accost this question and analyze the impact of Bt cotton adoption on calorie consumption and dietary quality in India. Bt cotton was first commercialized in India in 2002. In 2012, over 7 one thousand thousand farmers had adopted this applied science on x.8 one thousand thousand ha – equivalent to 93% of the country's full cotton surface area [10]. For the analysis, we carried out a household survey and collected comprehensive data over a catamenia of several years. This is the first ex post study that analyzes nutrient security effects of Bt cotton or any other GM crop with micro level data.
Materials and Methods
Ethics Statement
Our study builds on data from a socioeconomic survey of farm households in Republic of india. Details of this survey are explained farther below. The institutional review lath of the University of Goettingen only reviews clinical research; our written report cannot be classified as clinical research. We consulted with the Head of the Research Department of the University of Goettingen, who confirmed that there is no institutional review board at our University that would crave a review of such survey-based socioeconomic research.
Subcontract Household Survey
Nosotros carried out a panel survey of Indian cotton farm households in four rounds betwixt 2002 and 2008. We used a multistage sampling procedure. Four states were purposively selected, namely Maharashtra, Karnataka, Andhra Pradesh, and Tamil Nadu. These 4 states comprehend a broad multifariousness of different cotton-growing situations, and they produce 60% of all cotton fiber in central and southern India [23]. In these four states, we randomly selected 10 cotton-growing districts and 58 villages, using a combination of census data and agronomical production statistics [eighteen], [19], [23]. Within each hamlet, we randomly selected farm households from complete lists of cotton producers. Sample households were visited individually, and the household head was taken through a face-to-confront interview, for which we used a structured questionnaire. The questionnaire covered a wide array of agronomical and socioeconomic information, such as input-output details in cotton fiber production, engineering science adoption, other income sources, and household living standards. The interviews were carried out in local languages past a small team of enumerators, who were trained and supervised by the researchers.
Prior to starting each interview, the study objective was explained. We also clarified that the information collected would be treated confidentially, analyzed anonymously, and exist used for research purposes merely. Based on this, the interviewees were asked for their exact informed consent to participate. We decided non ask for written consent, because the interviews were non associated with any risk for participants. Furthermore, many of the sample farmers had relatively low educational backgrounds and were not used to formal paperwork. Very few households did not hold to participate; they were replaced with other randomly selected households in the aforementioned villages.
The first-round survey interviews took place in early 2003, shortly afterward the cotton fiber harvest for the 2002 flavor was completed. The same survey was repeated at two-year intervals in early 2005 (referring to the 2004 cotton flavor), early 2007 (referring to the 2006 flavour), and early 2009 (referring to the 2008 flavor). In total, 533 households were interviewed during the 7-year catamenia. Most of these households were visited in several rounds. The full sample consists of 1431 household observations (Table i). In 2002, the proportion of Bt adopters was nonetheless relatively small, only it increased rapidly in the post-obit years. By 2008, 99% of the sample households had adopted this engineering. To our noesis, this is the but longer-term panel survey of Bt cotton farm households in a developing country (the data fix with the variables used in this commodity is available every bit Data S1).
Table 1
Number of subcontract households sampled in Republic of india in four survey rounds.
| Farm households | 2002 | 2004 | 2006 | 2008 | Total |
| Adopters of Bt | 131 | 246 | 333 | 375 | 1085 |
| Non-adopters of Bt | 210 | 117 | 14 | five | 346 |
| Total | 341 | 363 | 347 | 380 | 1431 |
Calorie Consumption Information
The survey questionnaire included a detailed food consumption recall, which is a common tool to assess food security at the household level [24]. For a thirty-24-hour interval recall flow, households were asked near the quantity consumed of unlike food items and the respective monetary value. The questions covered food consumed from own production, market purchases, gifts, and transfers.
The quantity data for the different nutrient items were converted to calories consumed by using calorie conversion factors for Republic of india [25], [26]. The full household calorie consumption from the xxx-day think was then divided by 30 to obtain a calorie value per day. Taking into account the historic period and gender structure of households, likewise as physical action levels of household members, the number of developed equivalents (AE) was calculated for each household. Male adults involved in farming count as one.0 AE, female adults involved in farming every bit 0.8 AE. Male and female adults with lower physical action levels count as 0.8 and 0.seven, respectively. For children and adolescents, appropriate adjustments were made [25]–[27]. The daily household calorie consumption was divided by the number of AE in a household to obtain the calories consumed per AE and 24-hour interval.
Values for minimum dietary energy requirements plant in the literature vary, which is due to several reasons [24]. Values stated per capita are lower than those stated per AE, considering children take lower calorie requirements than adults. Moreover, not all studies take physical action levels into account already in the AE calculations, as we do. The boilerplate daily calorie requirement for a moderately active AE in Bharat is 2875 kcal/twenty-four hour period [25]. According to the World Health Organisation, a rubber minimum daily intake should not autumn below 80% of the calorie requirement, meaning 2300 kcal per AE. Minimum values around 2300 kcal per day for adult men are also found in other studies [28]. Based on this, we accept 2300 kcal per AE every bit the threshold, that is, households with daily calorie consumption below 2300 kcal per AE are considered food insecure.
Well-nigh of the calories consumed in rural India are from cereals such as wheat, rice, millet, and sorghum that are rich in carbohydrates but less nutritious in terms of poly peptide and micronutrient contents. Hence, in improver to full calories consumed nosotros calculated the number of calories consumed from more nutritious foods to assess dietary quality. In the category "more than nutritious foods", nosotros include pulses, fruits, vegetables, and all animal products (i.e., milk, milk products, meat, fish, and eggs). Recent research suggests that the share of calories consumed from higher value, not-staple foods can also be used every bit an indicator of nutritional sufficiency [29]. The reason is that poor and undernourished households volition largely choose foods that are the cheapest available sources of calories, namely cereals in the context of rural India. Only when they have surpassed subsistence, consumers volition brainstorm to substitute towards foods that are more than expensive sources of calories [29].
It should be mentioned that nutrient consumption data from household surveys may not provide very accurate information to measure nutritional status [24], [30]. Sometimes, consumption data overestimate calorie intakes, considering nutrient losses, waste, and other uses within the household cannot be properly accounted for. However, this limitation applies to both adopters and non-adopters of Bt, so that the comparison between Bt and not-Bt, which is relevant for the impact assessment, is unaffected.
Regression Models
To gauge the bear upon of Bt cotton adoption on calorie consumption, we backslide total daily calorie consumption per AE on Bt adoption, measured equally the number of hectares of Bt cotton grown past a household in a particular year. Since Bt adoption increases subcontract profits and household incomes [23], nosotros look a positive and significant treatment upshot. However, calorie consumption is also influenced by other factors that need to be controlled for. Nosotros control for didactics of the household head (measured in terms of the number of years of schooling); education plays an of import part for both income generation and consumption beliefs. We too include a variable for household size (measured in terms of AE). Moreover, we control for subcontract size in terms of expanse endemic, which is a proxy for agricultural asset ownership more generally. Farm income is not included in the model, as this is direct influenced past Bt adoption. Yet, off-farm income, measured in Usa$ per year, is controlled for. We also include state dummies for Karnataka, Andhra Pradesh, and Tamil Nadu (Maharashtra is the reference land), capturing climatic and agroecological differences. Given the panel structure of the data with four survey rounds, we use year dummies for 2004, 2006, and 2008 (2002 is the reference year).
Console data models are often estimated with a random furnishings computer [31]. All the same, a random furnishings estimator can lead to biased touch estimates when there is unobserved heterogeneity between Bt adopting and non-adopting households. Such bias resulting from endogeneity of the treatment variable is referred to as option bias in the impact assessment literature [23], [31]. Unobserved heterogeneity may potentially result from differences in household characteristics (e.g., Bt adopting farmers may have higher motivation, ameliorate management skills, or ameliorate access to information) or farm characteristics (e.thousand., differences in soil quality, or water access). Our panel information allow u.s.a. to control for such unobserved heterogeneity. Since we surveyed the same households repeatedly over a 7-year period when Bt adoption increased, for many households we take observations with and without Bt adoption. Hence, we rely on a within household calculator, which is also called a fixed effects figurer. Differencing inside households with the fixed effects estimator eliminates time-invariant unobserved factors, so that they can no longer bias the affect estimates [31]. A Hausman test is used to confirm the ceremoniousness of the stock-still effects specification [19], [31].
We estimate an additional model using calories from more nutritious foods (i.e., pulses, fruits, vegetables, and animal products) instead of full calorie consumption equally dependent variable. This boosted model helps to clarify impacts of Bt cotton wool adoption on dietary quality. A positive coefficient for the treatment variable would indicate that Bt adoption increases the consumption of more nutritious foods, thus not just contributing to more calories only also to meliorate dietary quality.
Results and Discussion
Descriptive statistics are shown in Table 2. The boilerplate farm household owns 5 ha of country, without a significant departure between Bt adopters and non-adopters. Effectually half of this area is grown with cotton fiber. Other crops cultivated include wheat, millet, sorghum, pulses, and in some locations rice, amidst others. Households are relatively poor; average annual per capita consumption expenditures range betwixt 300 and 500 U.s.a.$.
Table 2
Descriptive statistics of farm households.
| Variables | Adopters of Bt (N = 1085) | Not-adopters of Bt (N = 346) |
| Farm size (ha) | 5.11 (5.85) | 4.85 (5.51) |
| Cotton fiber area cultivated (ha) | ii.35 (2.35) | two.79 (19.67) |
| Surface area cultivated with Bt cotton (ha) | 1.97*** (two.08) | 0.00 (0.00) |
| Age of farmer (years) | 45.58 (12.86) | 45.94 (12.36) |
| Instruction of farmer (years) | 7.58*** (iv.94) | vi.69 (v.03) |
| Per capita consumption expenditure (US$/yr) | 490.31*** (430.eighteen) | 311.72 (355.58) |
| Off-subcontract income (US$/yr) | 560.lxx (1455.44) | 504.27 (2289.87) |
| Calorie consumption per AE (kcal/day) | 3329.41*** (719.38) | 2829.88 (598.99) |
| Calories consumed from more than nutritious foods per AE (kcal/mean solar day)a | 703.89*** (374.90) | 638.89 (345.41) |
| Household size (AE) | 5.01 (2.42) | v.14 (2.24) |
| Food insecure households (%)b | 7.93*** | 19.94 |
Bt adopting households swallow significantly more than calories than non-adopting households, and a smaller proportion of them is food insecure (Effigy 1, Table ii). This suggests that the cash income gains through Bt adoption may have improved food security among cotton-producing households. Nonetheless, this unproblematic comparison does not even so prove a causal relationship.
Density functions of household calorie consumption for adopters and non-adopters of Bt cotton fiber.
Functions were estimated not-parametrically using the Epanechnikov kernel with 1085 and 346 observations for adopting and non-adopting households, respectively. AE: developed equivalent.
Bear upon of Bt Cotton Adoption on Food Security
To further analyze the relationship between Bt adoption and calorie consumption, we employ panel regression models, as explained above. The master explanatory variable of interest is the Bt cotton area of a subcontract household, for which descriptive statistics are shown in Table iii. The boilerplate Bt area amidst technology adopters in the sample is close to 2 ha, which is equivalent to 85% of the total cotton area of these farms. A breakdown by survey twelvemonth shows that the average Bt area increased from less than 1.0 ha in 2002 to ii.4 ha in 2008. Hence, non but the number of Bt adopters but also the Bt surface area per adopting household increased considerably over time.
Table 3
Bt cotton surface area among adopting households.
| 2002 | 2004 | 2006 | 2008 | Total | |
| Mean Bt expanse (ha) | 0.94 | i.64 | 2.15 | 2.37 | ane.97 |
| Standard deviation | 1.32 | 1.87 | two.14 | 2.22 | 2.08 |
| Number of observations | 131 | 246 | 333 | 375 | 1085 |
The regression results are shown in Tabular array 4. Each ha of Bt cotton fiber has increased total calorie consumption by 74 kcal per AE and day. For the average adopting household, the internet consequence is 145 kcal per AE (Figure 2), implying a 5% increase over hateful calorie consumption in not-adopting households. Most of the calories consumed in rural India stem from cereals that are rich in carbohydrates but less nutritious in terms of protein and micronutrients. Yet the results prove that Bt adoption has significantly increased the consumption of calories from more than nutritious foods, thus also contributing to improved dietary quality.
Net effects of Bt adoption on household calorie consumption.
Results based on calorie consumption regression models estimated with panel data and household fixed furnishings (inside estimator). Total model results are shown in Table 4. Calories from more nutritious foods include pulses, fruits, vegetables, and beast products. Effects for the average adopting household accept into account the number of ha of Bt cotton actually grown. **Significant at the 5% level. ***Significant at the ane% level.
Tabular array 4
Calorie consumption models.
| Model (one) | Model (2) | Model (3) | |
| Variables | Total calories (RE model) | Total calories (FE model) | Calories from more than nutritious foods (Atomic number 26 model) |
| Bt expanse (ha) | 79.08*** (18.85) | 73.71*** (21.xl) | 23.17** (10.05) |
| Farm size (ha) | 9.27** (4.22) | −0.69 (7.80) | 1.97 (3.56) |
| Education of farmer (years) | 9.41** (four.40) | – | – |
| Off-subcontract income (United states of america$/year) | 0.07*** (0.02) | 0.05*** (0.02) | 0.01* (0.007) |
| Household size (AE) | −62.48*** (10.71) | −89.46*** (14.43) | −29.33*** (six.89) |
| Karnataka (dummy)a | 88.36 (57.97) | – | – |
| Andhra Pradesh (dummy)a | 21.46 (58.00) | – | – |
| Tamil Nadu (dummy)a | 212.86** (84.56) | – | – |
| 2004 (dummy)b | −34.35 (48.97) | −5.98 (51.60) | −45.25* (25.33) |
| 2006 (dummy)b | 13.68 (54.48) | thirty.09 (61.12) | −112.87*** (29.41) |
| 2008 (dummy)b | −92.92 (60.51) | −74.59 (69.51) | −72.lxx** (30.xx) |
| Constant | 3229.31*** (90.46) | 3537.08*** (78.16) | 843.23*** (41.42) |
| Number of observations | 1431 | 1431 | 1431 |
| R2 | 0.13 | 0.09 | 0.10 |
| Hausman exam (chi-square statistic) | 16.82** | ||
We applied the total calorie consumption issue of Bt to the subsample of non-adopters to simulate the nutrient security impact of adoption: if all not-adopters switched to Bt, the proportion of food insecure households would drop by 15–twenty% (Tabular array v). Almost of these nutritional benefits have materialized already, as over 90% of all cotton farm households in India accept adopted Bt technology by at present.
Tabular array five
Impact of Bt adoption on food security amid cotton-producing households.
| Food insecure households (%)a | Change in nutrient insecurity relative to condition quo (%) | |
| Non-adopters of Bt cotton wool (status quo) | nineteen.94 | |
| If non-adopters adopted Bt on their total cotton area | fifteen.90 | −twenty.26 |
| If non-adopters adopted Bt on 85% of their cotton area | xvi.76 | −15.95 |
Robustness Checks
We tested the robustness of the Bt effects by estimating calorie consumption models with culling specifications. These boosted estimates are shown in Table 6. Nosotros first look at possible changes in touch on over fourth dimension. In model (1), the Bt area variable is split into two periods, namely 2002–04 and 2006–08. In both periods, the Bt touch on calorie consumption was positive and pregnant, but the effect was bigger in 2002–04 than in 2006–08. The reason for this alter is not that income furnishings of Bt adoption would shrink; recent research showed that the profit gains of Bt cotton in India were constant or even increased over time [23]. The change in the calorie effect per ha of Bt is rather due to the fact that the Bt expanse per farm increased considerably in the later period, as was shown higher up. Measured per farm household, the calorie consumption outcome of Bt was really very like in 2002–04 and 2006–08.
Table 6
Robustness checks of Bt furnishings with unlike model specifications.
| Model (ane) | Model (2) | Model (3) | Model (iv) | |
| Variables | Total calories | Calories from more nutritious foods | Total calories | Total calories |
| Bt area 2002–04 (ha) | 135.25*** (28.95) | 17.94 (thirteen.24) | – | – |
| Bt area 2006–08 (ha) | 54.67** (23.33) | 24.79** (x.46) | – | – |
| Cumulative Bt surface area (ha) | – | – | 17.20 (12.xx) | −28.08** (13.21) |
| Bt area (ha) | – | – | – | 105.63*** (26.82) |
| Number of observations | 1431 | 1431 | 1431 | 1431 |
| Model (v) | Model (6) | Model (seven) | Model (8) | |
| Full calories | Total calories | Total calories | Total calories | |
| Bt area (ha) | 73.71*** (21.40) | 76.19*** (27.62) | 110.01*** (27.48) | 53.xl* (30.99) |
| Bt (dummy) | – | – | – | 599.84*** (70.29) |
| Number of observations a | 1431 | 1016 | 852 | 852 |
The smaller calorie consumption effect per ha of Bt with an increasing Bt area on a subcontract is consistent with Engel'south police, which states that the proportion of the household budget spent on food decreases as income rises [32]. Unsurprisingly, the same trend is not observed when we focus on higher value, non-staple foods. The results of model (ii) in Table vi suggest that the Bt consequence on calories from more nutritious foods has been increasing over fourth dimension. Hence, Bt cotton wool adoption leads to a lower staple calorie share, implying higher nutritional sufficiency and better dietary quality [29].
In model (3) of Table 6, we analyze whether the Bt issue is cumulative, meaning that households that have adopted Bt earlier or on larger areas benefit over-proportionally. This might be the case when profit gains from Bt adoption are reinvested, possibly entailing larger consumption benefits in subsequent periods. To exam for this option, we constructed a cumulative Bt area variable, adding upwards the Bt expanse on a subcontract in a particular year and Bt areas on the same farm in previous survey rounds. The coefficient of this variable is insignificant; cumulative effects exercise not seem to be important. If we include this variable together with the standard Bt expanse variable, the cumulative coefficient turns negative while the actual treatment effect increases (model four). Once again, this is consequent with Engel'south law, implying that larger areas with Bt pb to lower proportions of the income gains beingness spent on calories.
In models (half dozen) and (7), we analyze to what extent changes in the sample affect the interpretation results. For easy comparison, results from the total-sample reference model, which were discussed above, are repeated in model (5). It is sometimes observed that early adopters of a new engineering benefit more than than late adopters. This may be due to cumulative furnishings, which we already tested for. In improver, full general equilibrium adjustments may contribute to differential impacts between early and late adopters [33]. In model (6), we exclude all households that had adopted Bt already in the beginning survey round in 2002. The modify in the Bt effect is very small, and then we conclude that tardily adopters relish the same nutritional benefits per ha of Bt as early adopters.
This specification in model (6) with early adopters excluded is also an boosted robustness cheque for possible problems of endogeneity and choice bias. The stock-still effects console estimator controls for time-invariant heterogeneity between adopters and non-adopters of Bt. But it cannot command for possible fourth dimension-variant differences, which might play a function if early adopters are more than innovative also with respect to other opportunities non captured in our data. The similarity of the results in models (5) and (6) substantiates that the estimated Bt impacts do non suffer from selection bias. In model (seven), we exclude all observations of households that had adopted Bt in all survey rounds, so that the results are purely based on within household comparisons. The treatment event remains highly significant. It fifty-fifty increases in magnitude, suggesting that the full-sample result is rather a cautious, lower-jump gauge. Finally, model (8) includes a dummy for Bt adoption in addition to the Bt expanse variable used earlier. The dummy produces a big coefficient, underlining the positive food security impact of Bt adoption. Simply the Bt area result remains positive and significant, also, which confirms that using a continuous treatment variable is advisable.
Overall, the additional results with culling specifications strengthen the findings and show that the positive impacts of Bt cotton wool adoption on food security in India are very robust.
Conclusions
The results of this research confirm that the income gains through Bt cotton adoption amid smallholder farm households in India take positive impacts on food security and dietary quality. GM crops are not a panacea for the issues of hunger and malnutrition. Complex issues require multi-pronged solutions. But the show suggests that GM crops tin can exist an of import component in a broader food security strategy. And then far, food security impacts are still confined to simply a few concrete examples. The nutritional benefits could farther increase with more than GM crops and traits becoming available in the time to come. Appropriate policy and regulatory frameworks are required to ensure that the needs of poor farmers and consumers are taken into account and that undesirable social consequences are avoided.
Supporting Information
Acknowledgments
We thank Vijesh Krishna and 2 anonymous reviewers of this periodical for very useful comments.
Funding Statement
This enquiry was supported by the High german Research Foundation (DFG). The funders had no office in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3674000/
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