Understanding the climatic drivers of present-day agricultural yields is critical for prioritizing adaptation strategies to climate change. However, unpacking the contribution of different environmental stressors remains elusive in large-scale observational settings in part because of the lack of an extensive long-term network of soil moisture measurements and the common seasonal concurrence of droughts and heat waves. In this study, we link state-of-the-art land surface model data and fine-scale weather information with a long panel of county-level yields for six major US crops (1981–2017) to unpack their historical and future climatic drivers. To this end, we develop a statistical approach that flexibly characterizes the distinct intra-seasonal yield sensitivities to high-frequency fluctuations of soil moisture and temperature. In contrast with previous statistical evidence, we directly elicit an important role of water stress in explaining historical yields. However, our models project the direct effect of temperature—which we interpret as heat stress—remains the primary climatic driver of future yields under climate change.

This study examines the market drivers of corn monocropping in the U.S. Midwest by empirically analyzing crop rotation responses to market fluctuation from 2005 to 2014 and the price shock induced by the recent biofuel mandate. We find that the expected market returns for crops have a significant impact on farmers' decisions about monocropping. We also find that corn monocropping is loosely associated with the presence of nearby ethanol plants. This study illustrates the emerging use of high-resolution land cover data to tackle critical agribusiness and agro-environmental policy questions that remain elusive with aggregate data.

To meet future food demand and sustainability requirements of society, the agriculture sector faces challenges in both the institutional dimension and the technological dimension. One of the main concerns regarding the current agricultural production pattern is the tremendous amount of water it requires to maintain and boost output. With a changing climate and increasing demand from civil uses, promoting both water allocation efficiency and water application efficiency becomes the focus of policy design. The unintended consequences of water policies, however, have led to extensive debates. This study addresses the key question of whether irrigation efficiency improvement leads to reduced per-area water use. The study assembles a national county-level panel data set on water withdrawal, irrigation technology, and farm operation and demographics. The empirical results show that a higher irrigation efficiency is associated with a lower per-area water application in US crop production. Two alternative efficiency measures are proposed. Depending on how the efficiency is measured, a one standard-deviation efficiency improvement (6–30%) in irrigation can reduce 6–11% of water withdrawal in US crop production. The water saving is about 0.06–0.12 mm/day given a county average irrigation water use of 1.07 mm/day.

Controlling for spatial heterogeneity and spatial dependence in farmland valuation models has gained substantial attention in recent literature. This paper proposes to derive the spatial structure of farmland values endogenously and semiparametrically based on the spatial competition theory. The paper assembles panel data of Pennsylvania county level farmland values between 1982 and 2012. A spatial autoregressive panel data model with spatial weights matrix endogenously incorporated is estimated. Out of sample predictions and non-nested statistical tests for model selection suggest that the fit and the predictability of hedonic farmland valuation models can be greatly improved.