Roderick M. Rejesus

Greater sensitivity to drought accompanies maize yield increase in the U.S. Midwest

Predicting Responses to Drought The U.S. Corn Belt accounts for a sizeable portion of the worlds maize growth. Various influences have increased yields over the years. Lobell et al. (p. 516; see the Perspective by Ort and Long) now show that sensitivity to drought has been increasing as well. It seems that as plants have been bred for increased yield under ideal conditions, the plants become more sensitive to non-ideal conditions. A key factor may be the planting density. Although todays maize varieties are more robust to crowding and the farmer can get more plants in per field, this same crowding takes a toll when water resources are limited. Selective breeding focused on increasing corn and soybean yields has left a weakness in corn drought tolerance. [Also see Perspective by Ort and Long] A key question for climate change adaptation is whether existing cropping systems can become less sensitive to climate variations. We use a field-level data set on maize and soybean yields in the central United States for 1995 through 2012 to examine changes in drought sensitivity. Although yields have increased in absolute value under all levels of stress for both crops, the sensitivity of maize yields to drought stress associated with high vapor pressure deficits has increased. The greater sensitivity has occurred despite cultivar improvements and increased carbon dioxide and reflects the agronomic trend toward higher sowing densities. The results suggest that agronomic changes tend to translate improved drought tolerance of plants to higher average yields but not to decreasing drought sensitivity of yields at the field scale.

Factors Affecting Farmers' Utilization of Agricultural Risk Management Tools: The Case of Crop Insurance, Forward Contracting, and Spreading Sales

Factors affecting the adoption of crop insurance, forward contracting, and spreading sales are analyzed using multivariate and multinomial probit approaches that account for simultaneous adoption and/or correlation among the three risk management adoption decisions. Our empirical results suggest that the decision to adopt crop insurance, forward contracting, and/or spreading sales are correlated. Richer insights can be drawn from our multivariate and multinomial probit analysis than from separate, single-equation probit estimation that assumes independence of adoption decisions. Some factors significantly affecting the adoption of the risk management tools analyzed are proportion of owned acres, off-farm income, education, age, and level of business risks.

U.S. Agricultural Producer Perceptions of Climate Change

This study examines U.S. crop producers’ perceptions of climate change, its effects on crop agriculture, and likely ways farmers would adapt to weather extremes. Based on a survey of crop producers in four states, we find that a significant proportion of farmers do not perceive that climate change has been scientifically proven and do not believe that it will adversely affect average crop yields and yield variability. Farmers are likely to diversify crops, buy crop insurance, modify lease arrangements, and exit farming in response to extreme weather caused by climate change.

Binary choice models for rare events data: a crop insurance fraud application

This study implements a recently proposed score test that could help guide insurance fraud researchers in deciding whether to use a logit or a probit model in predicting insurance fraud probabilities, especially when the occurrence of ones in the dependent variable is much less than zeros. The test is easily implemented in a crop insurance fraud context and seems to be a promising method that could be applicable to analysing and detecting potentially fraudulent claims in various lines of insurance.

The Dynamics of Exports and Productivity at the Plant Level: A Panel Data Error Correction Model (ECM) Approach

This article examines the short-run and long-run dynamics of the export-productivity relationship for Turkish manufacturing industries. We use an error correction model (ECM) estimated using a system Generalized Method of Moments (GMM) estimator to achieve this objective. Our results suggest that permanent productivity shocks generate larger long-run export level responses, as compared to long-run productivity responses from permanent export shocks. This result suggests that industrial policy should be geared toward permanent improvements in plant-productivity in order to have sustainable long-run export and economic growth.

Is there Evidence of Learning‐by‐Exporting in Turkish Manufacturing Industries?

Abstract Exporting has always been thought of as one tool to improve productivity and, consequently, to spur economic growth in low‐ to middle‐income economies. However, empirical evidence of this so‐called ‘learning‐by‐exporting’ effect has been limited. This article determines whether learning‐by‐exporting is evident in two Turkish manufacturing sectors—the textile and apparel (T&A) and the motor vehicle and parts (MV&P) industries. A semi‐parametric estimator that controls for problems associated with simultaneity and unobserved plant heterogeneity is used to test the learning‐by‐exporting hypothesis. After controlling for these issues, our results suggest statistically stronger learning‐by‐exporting effects in the T&A than in the MV&P industry. The highly concentrated and capital‐intensive nature of the MV&P industry is the main reason for the lower learning‐by‐exporting effect in this sector. From a policy perspective, this implies that targeting export‐enhancing policies to industries with significant learning‐by‐exporting effects may lead to more productivity gains and would better stimulate an export‐led growth.

Safety Nets or Trampolines? Federal Crop Insurance, Disaster Assistance, and the Farm Bill

We review the implications of the 2007 Farm Bill for the risk management dimensions of U.S. agriculture and policy. Legislative proposals suggest significant changes in risk management policy, including the introduction of state or national revenue insurance. We also pursue an empirical analysis of the interrelationships of crop insurance, disaster relief, and farm profitability. We find an inverse relationship between disaster assistance and insurance purchases. Our analysis also suggests that farmers that buy insurance and that receive disaster payments tend to have higher returns to farming.

Developing Experience-Based Premium Rate Discounts in Crop Insurance

This article addresses the feasibility of implementing an experience-based premium rate discount system in crop insurance. While adverse selection and moral hazard in crop insurance have been extensively studied in the past, discount systems or bonus-malus incentives have not, to our knowledge, been investigated. Our empirical analysis indicates that a crop insurance discount system could be implemented based on a measure of favorable past insurance experience. The estimated average discounts based on the rating methods developed in this study ranged from 5% to 9% (depending on the crop being considered).

Timing of precision agriculture technology adoption in US cotton production

The timing of technology adoption is influenced by profitability and farmer ability to bear risk. Innovators are typically more risk tolerant than laggards. Understanding the factors influencing early adoption of precision agriculture (PA) technologies by cotton farmers is important for anticipating technology diffusion over time. The factors influencing the timing of grid soil sampling (GSS), yield monitoring (YMR) and remote sensing (RMS) adoption by cotton producers was evaluated using multivariate censored regression. Data for cotton farmers in 12 states were obtained from a survey conducted in 2009. The factors hypothesized to influence the timing of adoption included farm characteristics, operator characteristics, PA information sources, adoption of other PA technologies, and farm location. The results suggest that different factors influenced when cotton farmers adopted GSS, YMR and RMS after these technologies became commercially available. For example, land ownership was associated with the timing of GSS adoption, but not YMR or RMS adoption; farmer age was correlated with the timing of GSS and YMR adoption, but not RMS adoption; and obtaining PA information from consultants affected the timing of GSS and RMS adoption, but not YMR adoption. The only factors correlated with the early adoption of all three technologies were beliefs that PA would improve environmental quality and the adoption of at least one other PA technology. Thus, the potential for improved environmental quality appears to be a strong adoption motivator across PA technologies, as is the earlier adoption of other PA technologies. This research may be useful for farmers, researchers, Extension personnel, machinery manufacturers, PA information providers and agricultural retailers to anticipate the future adoption of new and emerging PA technologies.

Estimating the demand and willingness-to-pay for cotton yield monitors.

Survey data from cotton farmers in six southeastern states of the USA were used to estimate the demand and willingness-to-pay (WTP) for either retrofitting yield monitors onto cotton pickers or to purchase a yield monitor as an option with a new cotton picker. ‘Don’t know’ responses were either omitted, combined with ‘no’ responses or included as a separate category for comparing WTP and estimates of the price elasticity of demand. Our results suggest that treating the ‘don’t know’ response as a separate category provides WTP estimates that are more consistent with expectations than the other approaches. The estimated price elasticities and demand curves indicate that previous users of precision technology are more responsive to changes in price of cotton yield monitors and would be more likely to adopt them when the price decreases. These demand and WTP estimates provide important information that can be used by those who sell cotton yield monitors, as well as policy-makers who may wish to subsidize this technology. Referendum contingent valuation was useful for evaluating the demand for any new technology.