Crop simulation models are valuable tools for quantifying crop yield response to water, and for devising strategies to improve agricultural water management. However, applicability of the majority of crop models is limited greatly by a failure to provide open-access to model source code. In this study, we present an open-source version of the FAO AquaCrop model, which simulates efficiently water-limited crop production across diverse environmental and agronomic conditions. Our model, called AquaCrop-OpenSource (AquaCrop-OS), can be run in multiple programming languages and operating systems. Support for parallel execution reduces significantly simulation times when applying the model in large geospatial frameworks, for long-run policy analysis, or for uncertainty assessment. Furthermore, AquaCrop-OS is compliant with the Open Modelling Interface standard facilitating linkage to other disciplinary models, for example to guide integrated water resources planning.This research was funded in part by USDA NIFA Award number 2012-67003-23227 and by a PhD studentship from the Grantham Institute at Imperial College London. Further support was provided by the Daugherty Water for Food Global Institute at the University of Nebraska.Peer reviewe

Reliable accounting of agricultural water use is critical for sustainable water management. However, the majority of agricultural water use is not monitored, with limited metering of irrigation despite increasing pressure on both groundwater and surface water resources in many agricultural regions worldwide. Satellite remote sensing has been proposed as a low‐cost and scalable solution to fill widespread gaps in monitoring of irrigation water use in both developed and developing countries, bypassing the technical, socioeconomic, and political challenges that to date have constrained in situ metering. In this paper, we show through a systematic meta‐analysis that the relative accuracy of different satellite‐based irrigation water use monitoring approaches remains poorly understood, with evidence of large uncertainties when water use estimates are validated against in situ irrigation data at both field and regional scales. Subsequently, we demonstrate that water use measurement errors result in large economic welfare losses for farmers and may negatively impact ability of policies to limit acute and nonlinear externalities of irrigation abstraction on both the environment and other water users. Our findings highlight that water resource planners must consider the trade‐offs between accuracy and costs associated with different water use accounting approaches. Remote sensing has an important role to play in supporting improved agricultural water accounting—both independently and in combination with in situ monitoring. However, greater transparency and evidence is needed about underlying uncertainties in satellite‐based models, along with how these measurement errors affect the performance of associated policies to manage different short‐ and long‐term externalities of irrigation water use.

Weather shocks, such as heatwaves, droughts, and excess rainfall, are a major cause of crop yield losses and food insecurity worldwide. Statistical or process-based crop models can be used to quantify how yields will respond to these events and future climate change. However, the accuracy of weather-yield relationships derived from crop models, whether statistical or process-based, is dependent on the quality of the underlying input data used to run these models. In this context, a major challenge in many developing countries is the lack of accessible and reliable meteorological datasets. Gridded weather datasets, derived from combinations ofin situgauges, remote sensing, and climate models, provide a solution to fill this gap, and have been widely used to evaluate climate impacts on agriculture in data-scarce regions worldwide. However, these reference datasets are also known to contain important biases and uncertainties. To date, there has been little research to assess how the choice of reference datasets influences projected sensitivity of crop yields to weather. We compare multiple freely available gridded datasets that provide daily weather data over the Indian sub-continent over the period 1983–2005, and explore their implications for estimates of yield responses to weather variability for key crops grown in the region (wheat and rice). Our results show that individual gridded weather datasets vary in their representation of historic spatial and temporal temperature and precipitation patterns across India. We show that these differences create large uncertainties in estimated crop yield responses and exposure to variability in growing season weather, which in turn, highlights the need for improved consideration of input data uncertainty in statistical studies that explore impacts of climate variability and change on agriculture.