Agricultural Economics最新文献

Pesticides are used to reduce yield losses and to enhance the visual quality of products. However, pesticide use raises concerns due to negative health and environmental effects, hence ambitious policy goals for their reduction have been established. Reducing pesticides which mainly focus on visual quality of products could be an efficient strategy to contribute to these goals, without reducing food production. However, the role of “cosmetic” pesticide use is so far not well documented and understood. Here, we quantify cosmetic pesticide use and the influence of supply chain characteristics on their use. We focus on table apple production, where the visual quality of products is a key aspect. Using a sample of 196 apple growers in Switzerland, we find that 23.5%–59.2% of growers use cosmetic pesticides for the apples’ visual appearance. Farms mainly marketing via intermediaries are 23.9-29.6% more likely to spray cosmetic pesticides for visual purposes compared to farms mainly direct marketing. Our findings highlight the role of supply chains in farmer decision-making, recommending a decrease in the focus on visual product quality, especially in retail environments, thus minimizing unnecessary and irreversible risks of pesticide exposure by farmers without compromising food security.

The establishment of secured and easily transferable land use rights is a key component of rural development and poverty reduction pathways in many underdeveloped countries. In this study, we use balanced panel data collected from 407 farm households between 2009/10 and 2014/15 production seasons and apply difference-in-difference approach to examine impact of second-stage land certification on tenure security, participation and extent of participation in the land rental market. Our finding shows that second-stage land certification has helped to boost tenure security of smallholder farmers. However, the differences in difference results show that the second stage land certification has not yet started to influence farm households’ participation in the land rental market. This could probably be due to the fact that the program is at its early stage of implementation and may warrant further scrutiny with additional time.

Variable weather continues to be the major source of vulnerability to chronic hunger and poverty in many developing countries due to the strong dependency of livelihood strategies on rainfed farming. Quantifying the effect of climatic parameters on agricultural households is, therefore, necessary to help policymakers understand the benefits of climate policies, improve the allocation of the scarce resources dedicated to adaptation and prioritize among adaptation strategies. This article investigates the empirical relationship between the welfare of rural households and rainfall variability in the semi-arid tropics, using household panel data and high-resolution remotely sensed rainfall data from Niger. We find that a standard deviation increase in rainfall variability is associated with a reduction of real food consumption by 11.13%. Results also indicate that a standard deviation increase in rainfall variability reduces expenditures for cereal-based products, animal-based products and processed foods by 11.96%, 21.31%, and 16.23%, respectively. Our results are consistent across a battery of robustness checks. Finally, we find geographical-based differences in terms of the effect and that access to cereal banks cushions the negative effect of rainfall variability. Policy interventions aiming at improving the well-being of rural households should therefore emphasize improving climate adaptation strategies.

This article investigates the dynamic linkages between agricultural and energy markets, with a focus on an econometric analysis of multivariate stochastic dynamics based on the joint distribution of state variables. The analysis relies on a quantile approach followed by the evaluation of a copula. Applied to nonlinear price dynamics, the approach is flexible and supports a general evaluation of impulse response functions representing how prices adjust over time and across markets in response to a given shock. The analysis allows for arbitrary distribution functions; it captures own-price and cross-price dynamics that can depend on the nature of shocks; and it also allows current changes to affect all moments of the future price distributions. The usefulness of the approach is illustrated in an econometric investigation of dynamic linkages in US corn, ethanol, and crude oil markets. We show how price adjustments can vary across quantiles, reflecting different speeds of adjustments depending on market conditions. We find evidence of nonlinear dynamics specific to the tails of the price distributions. We uncover evidence of positive contemporaneous codependence, especially tail dependence. We show how price shocks affect mean, variance, skewness as well as kurtosis of future price distributions. These results stress the importance of going beyond a standard mean-variance analysis. They also shed new light on the deep linkages existing in the food-fuel nexus.

This article describes the construction of the Luenberger soil quality indicator (SQI) using data on crop yield, non-soil inputs, and soil profile from three irrigated agroecological zones of Punjab, Pakistan, namely, rice–wheat, maize–wheat–mix, and cotton–mix zones. Plot level data are used to construct a soil quality indicator by estimating directional distance functions within a data envelopment analysis (DEA) framework. We find that the SQI and crop yield relationships exhibit diminishing returns to improving soil quality levels. Using the constructed SQI values, we estimate linear regression models to generate weights that could be used directly to aggregate individual soil attributes into soil quality indicators without the necessity of fitting a frontier to the crop production data. For wheat and rice production, we find that SQI is most sensitive to changes in soil electrical conductivity (EC) and potassium (K). The SQI has direct relevance for site-specific decision-making problems where policymakers need to price land resources and conservation services to achieve agricultural and environmental goals.

This article investigates the effect of global oil and wheat prices, and local price shocks on the real price of wheat flour in Lebanon. We estimate a structural vector autoregressive model with exogenous variables (SVARX) using Bayesian methods. We then compute the impulse response functions and find that global commodity price shocks play a trivial role. Meanwhile, local gasoline price and wheat flour price-specific shocks trigger large increases in the Lebanese wheat flour price on impact. Furthermore, since 2020, local gasoline price and wheat flour price-specific shocks have contributed the most to the historical variation in the Lebanese wheat flour price.

We examine the impact of climate driven heat and water stress on aggregate crop production growth, paying particular attention to the Sub-Saharan Africa (SSA) region as opposed to studies with a global or Non SSA focus. Using gridded data on temperature and precipitation, which is crop weighted and averaged to the national level, we generate measures of stressors that capture average temperature and precipitation shocks, and extreme punctuated events like dry spells and heat waves for 38 countries in Sub Saharan Africa between 1979 and 2016. We find in general that compared to estimates with a global or non SSA focus, the detrimental effect of increased annual temperature has been overstated, while the damage caused by shorter-term extremes like dry spells and heat waves has been understated. This implies that region specific analysis is key in developing a more comprehensive understanding of climate change. Such analyses are pivotal for climate policy development allowing for more spatially efficient allocation of limited financial resources, and greater accuracy in estimating adaptation effects.

United States cattle producers have various government-sponsored programs to protect against weather and disease related risks, but livestock risk protection (LRP) insurance is the only program that protects against price risk. However, adoption of LRP insurance is low even though cattle price declines are the primary cause of economic loss, and LRP premium subsidies have recently been increased. Therefore, the objective of this study is to explore how informational nudges about receiving an indemnity payment, LRP contract characteristics, and individual risk preferences affect the use of LRP. Producer survey results were estimated using a Cragg model to determine the factors affecting producers’ likelihood of purchasing LRP and the number of head they would insure. Producers were more likely to purchase 100% LRP coverage and would also insure more head at 100% coverage when compared to lower coverage levels. We found providing information on the probability of receiving an indemnity did not impact LRP purchasing decisions. However, counter to expectations, producers were more likely to buy LRP when the randomly provided cattle prices in the survey were successively increasing each month, and if participants considered themselves more willing to take risks in their cattle operation. Results provide insights into behavioral factors affecting LRP participation which could help inform future insurance policies.

Accurate commodity price forecasts are crucial for stakeholders in agricultural supply chains. They support informed marketing decisions, risk management, and investment strategies. Machine learning methods have significant potential to provide accurate forecasts by maximizing out-of-sample accuracy. However, their inherent complexity makes it challenging to understand the appropriate data pre-processing steps to ensure proper functionality. This study compares the forecasting performance of Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs) with classical econometric time series models for corn futures prices. The study considers various combinations of data pre-processing techniques, variable clusters, and forecast horizons. Our results indicate that LSTM-RNNs consistently outperform classical methods, particularly for longer forecast horizons. In particular, our findings demonstrate that LSTM-RNNs are capable of automatically handling structural breaks, resulting in more accurate forecasts when trained on datasets that include such shocks. However, in our setting, LSTM-RNNs struggle to deal with seasonality and trend components, necessitating specific data pre-processing procedures for their removal.

Free-on-board (FOB) export prices for identical products from the same origin often differ across destinations, even when accounting for the trade costs and attributes of the destination country. One explanation for this observed price difference is per-unit trade costs, and the ability of exporters to vary their markups and/or product quality. Using a novel dataset that details trade flows between countries by mode of transport, we estimate the transport mode-specific effect of a per-unit trade cost, specifically specific tariffs, on the FOB export prices of agricultural products. We find an elasticity of specific tariffs to export prices of 1.8%. However, the estimates are heterogeneous across modes of transport. The elasticity of specific tariffs to export prices is 2% for air transport, 5% for road transport, and  .3% for sea cargo. Since the observed positive export price effect can reflect product quality differences or markups, we account for the quality element and find that for a given product quality, markups increase with increasing specific tariffs. This form of price discrimination is less pronounced for higher-quality products that are predominantly shipped by air.