Search results

This study analyzes the factors which affect the alfalfa cultivation acreage in China and estimates the development of alfalfa planting by the supply model.

Based on the characteristics and actual conditions of alfalfa cultivation in China, a naïve empirical model was created to analyze the impact of various influencing factors on the cultivation acreage of alfalfa.

The analysis of influential factors shows that China's alfalfa planting conforms to naïve price behavior. The prices of alfalfa and per capita arable land occupancy have a positive effect on the cultivation acreage, while the price of competitive crops and transportation costs have a negative effect on the production of alfalfa. Lastly, the 2012 alfalfa subsidy policy has a significant negative impact on alfalfa cultivation acreage.

Due to the limited research on alfalfa supply in China, there is a lack of available research data and statistical data. A large number of data in this study are mainly indirect data derived and calculated from other industrial data. The measurement results may not be fully accurate.

This study represents the first empirical analysis of the characteristics of the factors influencing alfalfa cultivation acreage in China. The secondary data were used to analyze the influence of various control variables on the cultivation acreage of alfalfa, which is different from existing research.

This paper aims to investigate how climatic, market and policy factors interact to determine food production in Togo. Specifically, we estimate acreage and yield response to market prices, weather and policy changes for maize and rice.

We use panel data estimators in a Seemingly Unrelated Regressions Equation (SURE) model with region-level data from the Food and Agriculture Organization statistics department and the National Oceanic and Atmospheric Administration (NOAA) of the US Department of commerce.

We found lower fertilizer price and higher grain price effects on maize acreage and yield. In addition, we found a positive effect of expected rice price on both its acreage and yield. As expected, rainfall during planting months has a significant impact on both maize (April) and paddy (May) acreage allocations. Similarly, total rainfall during the growing season has a positive impact on both maize and paddy yields. Moreover, recent agricultural policy initiative designed to boost domestic food production has significantly increased acreage and yield for maize, and yield for paddy, especially the strategy for agricultural growth.

The dataset includes region-level observations from 1991 to 2012 which limits the observation span. However, we had enough variability in key variables to determine the estimated coefficients.

Although the dataset is limited in time (1991–2012) and uses national-level output prices, this investigation reveals that cropland allocation to maize and rice is sensitive to fertilizer and grain prices, weather expectations and policy interventions. These findings provide evidence for sustainable food production and productivity enhancement in Togo.

Understanding drivers of cropland allocation and cereal yield contribute to better food security and poverty reduction in developing countries, especially Togo.

Prior to this study, little was known on the effect of price, climate and policy on cropland allocation in Togo. This investigation contributes significantly to filling this knowledge gap and provides insights for effective interventions.

The purpose of this paper is to examine the impacts of farmland renting-in on planted grain acreage.

A survey data of five counties were analyzed with the two-stage ordinary least squares model.

Households renting-in land trended to plant more maize, and the more land was rented by a household the more maize was planted, while wheat acreage showed non-response to farmland renting-in.

Overall, the analysis suggests that policy makers should be prepared for different changing trends of grain crop acreage across the nation as farmland transfer continues. Future research should pay attention to the effect of farmland transfer on agricultural productivity and rural household income growth.

As the Chinese Government is promoting larger-scale and more mechanized farms as a way of protecting grain security, it is important to understand whether farmland renting-in will reduce planted grain acreage. This study provides empirical evidence showing the answer to that question may differ across different regions and depend on the particular grain crop in question.

The purpose of this paper is to analyze the effects of data aggregation and farm‐level crop acreage on the level of natural hedge, i.e. the level of price‐yield correlations, which is an important issue in risk modeling and management.

Swiss FADN data for five crops covering the period 2002‐2009 are used to estimate price‐yield correlations at the farm‐ as well as on an aggregated level. Tobit regressions are used to estimate empirical relationships between the level of natural hedge and the underlying crop acreage.

Price‐yield correlations differ significantly between farm‐ and aggregated‐level. More specifically, the natural hedge observed at the farm‐level is much smaller, i.e. correlations are closer to zero. Taking correlations from aggregated levels thus leads to an underestimation of farm‐level revenue variability. Furthermore, it is found that larger farms have a stronger natural hedge. For instance, a 1 percent increase in area under maize and intensive barley leads to a change in the correlation by −0.02 and −0.08, respectively.

The natural hedge is often approximated with correlations observed at more aggregated levels, e.g. the county level. The results show that this implies errors in risk assessment and modeling as well as insurance applications. Thus, farm‐level estimates should be used. The here presented relationship between price‐yield correlations and farm‐level crop acreage can be used to derive better information on levels of the natural hedge.

Even though the effects of data aggregation on price‐yield correlations have been discussed in earlier research, this paper is the first to also account for on‐farm effects of underlying crop acreage on levels of natural hedge. It is found that this simple relationship can be useful in risk management and modeling applications.

Fewer researchers have investigated the climatic and economic drivers of land-use change simultaneously and the interplay between drivers. This paper aims to investigate the nonlinear and interaction effects of price and climate variables on the rice acreage in high-latitude regions of China.

This study applies a multivariate adaptive regression spline to characterize the effects of price and climate expectations on rice acreage in high-latitude regions of China from 1992 to 2017. Then, yield expectation is added into the model to investigate the mechanism of climate effects on rice area allocation.

The results of importance assessment suggest that rice price, climate and total agricultural area play an important role in rice area allocation, and the importance of temperature is always higher than that of precipitation, especially for minimum temperature. Based on the estimated hinge functions and coefficients, it is found that total agricultural area has strong nonlinear and interaction effects with climate and price as forms of third-order interaction. However, the order of interaction terms reduces to second order after absorbing the expected yield. Additionally, the marginal effects of driven factors are calculated at different quantiles. The total area shows a positive and increasing marginal effect with the increase of total area. But the positive impact of price on the rice area can only be observed when price reached 50% or higher quantiles. Climate variables also show strong nonlinear marginal effects, and most climatic effects would disappear or be weakened once absorbing the expected rice yield. Expected yield is an efficient mechanism to explain the correlation between crop area and climate variables, but the impact of minimum temperature cannot be completely modeled by the yield expectation.

To the best of the authors’ knowledge, this is the first study to examine the nonlinear response of land-use change to climate and economic in high-latitude regions of China using the machine learning method.

The purpose of this paper is to analyze the heterogeneity of regional grain production distribution in China, by examining the regional heterogeneity of absolute and relative changes in grain planting acreage, and explain it in terms of increasing labor costs and difficulties in agricultural inputs adjustment.

The study uses data from official statistical yearbooks and the satellite remote sensing image data of Landsat TM 30 m. Multivariate analysis is conducted to examine the effect of labor cost, difficulty in replacing agricultural input factors and other factors underpinning changes in grain acreage and grain structure adjustment.

The heterogeneity of changes in grain acreage and proportion of arable area for grain production are mainly determined using the labor cost and difficulties in the replacement of agricultural input factors.

To the best of the authors’ knowledge, this study is the first to systematically analyze the heterogeneity in restructuring grain production at provincial level and its causes. The results not only provide evidence of grain production restructuring at regional level, but also contribute to the understanding of the law of structural change in agricultural production.

While genetically modified (GM) crops have provided tremendous agricultural productivity gains, many consumers oppose GM products and maintain they are unsafe. We use the case of GM sugar beets and their adoption by the US producers to examine the implications of GM technology on food security. A partial equilibrium framework is used to examine the implications of GM technology on food security. This analysis provides a unique opportunity to examine the impact of GM adoption in one product (sugar beets) relative to non-GM adoption in a substitute product (sugarcane). This analysis examines the potential gains to food security through the adoption of biotechnology versus consumer fear of GM technology. Research and development (R&D) has potential implications not only through its impact on supply, but also on demand as well. This study shows that demand impacts can negate the supply-induced food security gains of R&D. Regulations such as mandatory labeling requirements can impact this outcome.

The global demand for agricultural commodities may more than double in the second half of the twenty-first century. It has been suggested that rapidly growing world needs in food and agriculture can be met by expanding the acreage or cultivation of existing farmland. Because available land for farming is limited, about 90% of future production growth is expected to result from yield growth, with only 10% realized at the cost of acreage expansion.

In this chapter, we analyze the multitude of social benefits of modern agriculture. We also expand the traditional analysis of the return to research methodology by explicitly including environmental and other benefits of crop yield growth.

A key result of our analysis is that the environmental benefits of productivity growth far exceed the direct economic benefits to consumers and producers from an expansion of production. Hence, restricting analysis solely to price and quantity effects seriously underestimates the social benefits of modern agriculture.

The environmental benefits of yield growth in modern agriculture far exceed the traditional measure of social welfare.

The purpose of this paper is to explore the potential causes of the reduction of cotton cultivation in Shandong Province of China from the perspective of smallholders and notably examine the role of off-farm employment.

This paper extends an integrated behavioral model to analyze the relationship between off-farm employment and cotton cultivation by taking into account farmers’ risk attitudes. A household survey data of 144 Bt cotton farmers in six villages in Linqing County, Shandong Province conducted in 2012 and 2013 is used. A simultaneous equations model is established and further estimated by using three-stage least squares method.

Although the introduction of Bt cotton has promoted the increase in cotton acreage in China from 1999 to 2007, the planting area of cotton has been decreasing since 2007. The results show the significant correlations among risk attitude, off-farm employment, and cotton cultivation. The planting area of cotton is positively correlated with farmers’ willingness to take risk but negatively associated with off-farm employment of family members. The findings imply that the rapid emergence of off-farm labor markets is a major reason for the reduction of cotton acreage in Shandong Province. In the context of the more opportunities of off-farm employment in China, cotton acreage is expected to decrease further.

The findings provide a reasonable explanation for the reduction of cotton cultivation in Shandong. This analysis contributes to a better understanding of the relationships among individual risk attitude, off-farm employment, and agricultural behavior, thereby adding to the literature about the application of the integrated behavioral model.