Agricultural Economics最新文献

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.

How does the receipt of a cash transfer impact consumption of nutrients, vitamins, and minerals in households? To answer this question, we use a randomized controlled trial dataset from Hunger Safety Net Program (HSNP) with 9,246 households spread across the four districts (Turkana, Marsabit, Wajir, and Mandera) of Kenya. In the experiment, HSNP treated households received a bi-monthly cash transfer of about United States of America Dollar (USD) 20 relative to households in control sub-locations. Using difference in-difference specification, we find that HSNP poor beneficiary households in treated households increased (by approximately 96%, 50%, and 61%) the consumption of vitamins A, C, and beta carotene, respectively compared to those in control sub-locations. Moreover, HSNP non-poor, non-beneficiary households residing in treated sub-locations increased (by about 70% and 46%) the consumption of vitamin A and Beta carotene, respectively compared to those in control sub-locations. In addition, HSNP-poor beneficiary households in treated sub-locations sourced most of their nutrients, vitamins, and minerals from the market. We rule out alternative pathways that could potentially increase consumption and conclude that a rise in consumption amongst HSNP non-poor, non-beneficiary households is due to sharing of HSNP transfer amongst social network members.

In this study, we enhance our understanding of the economic impacts of climate change on agriculture in Iran to provide further information for moving Iran's climate policy forward by linking farmland net revenue to novel climatic and non-climatic variables. We take advantage of spatial panel econometrics to better circumvent omitted factors extraneous to the agricultural sector and to develop a more reliable and consistent model when data are inherently spatial. In contrast to conventional panel studies which relied on year-to-year weather observations, we exploit a hybrid approach to compromise between the disadvantages and advantages of longer-term cross-sectional analysis and shorter-term panel models. We estimate the potential impacts of climate change on agriculture under several global warming scenarios based on the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6). We find that (I) farmlands’ net revenues are projected to decline by 8%–19% and 14%–51% by 2050 and 2080; (II) the distributional impacts of climate change would highly depend on climate zones and geographical locations; (III) a few counties might benefit from climate changes; (IV) finally, failing to account for spatial spillovers when they are present leads to a misspecified model.