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.

Data-driven decision making in agriculture can be augmented by utilizing the data gathered from precision agriculture technologies to make the most informed decisions that consider spatiotemporal specificity. Decision support systems utilize underlying models of crop responses to generate management recommendations, yet there is uncertainty in the literature on the best model forms to characterize crop responses to agricultural inputs likely due for the most part to the variability in crop responses to input rates between fields and across years. Seven fields with at least three years of on-farm experimentation, in which nitrogen fertilizer rates were varied across the fields, were used to compare the ability of five different model types to forecast crop responses and net-returns in a year unseen by the model. All five model types were fit for each field using all permutations of the three years of data where two years were used for training and a third was held out to represent a “future” year. The five models tested were a frequentist based non-linear sigmoid function, a generalized additive model, a non-linear Bayesian regression model, a Bayesian multiple linear regression model and a random forest regression model. The random forest regression typically resulted in the most accurate forecasts of crop responses and net-returns across most fields. However, in some cases the model type that produced the most accurate forecast of grain yield was not the same as the model producing the most accurate forecast of grain protein concentration. Models performed best when the data used for training models was collected from years with similar weather conditions to the forecasted year. The results are important to developers of decision support tools because the underlying models used to simulate management outcomes and calculate net-returns need to be selected with consideration for the spatiotemporal specificity of the data available.

Online crowdfunding can help address the perennial financial shortfalls in environmental conservation and management. Although many online crowdfunding campaigns fail to collect any funds due to not achieving their targets, little is known about what drives success. To address this knowledge gap, we applied a mixed-methods approach to data from 473 successful and failed campaigns hosted on the online crowdfunding platform Readyfor. We found that fundraising performance varied by topic, with campaigns on pet animal management outperforming those focussed on landscape management and sustainable use. We also found that marketing strategies associated with online findability and increased reach through social networks, increased fundraising success. However, the existence of other environmental campaigns running simultaneously, reduced the chance of success, which implies that the selecting popular topics does not always increase the likelihood of success due to increased competition. Wider applications of marketing could enhance the ability of environmental crowdfunding campaigns to raise funds.