Crop yields in smallholder systems are traditionally assessed using farmer-reported information in surveys, occasionally by crop cuts for a sub-section of a farmer's plot, and rarely using full-plot harvests. Accuracy and cost vary dramatically across methods. In parallel, satellite data is improving in terms of spatial, temporal, and spectral resolution needed to discern performance on smallholder plots. This study uses data from a survey experiment in Uganda, and evaluates the accuracy of Sentinel-2 imagery-based, remotely-sensed plot-level maize yields with respect to ground-based measures relying on farmer self-reporting, sub-plot crop cutting (CC), and full-plot crop cutting (FP). Remotely-sensed yields include two versions calibrated to FP and CC yields (calibrated), and an alternative based on crop model simulations, using no ground data (uncalibrated). On the ground, self-reported yields explained less than 1 percent of FP (and CC) yield variability, and while the average difference between CC and FP yields was not significant, CC yields captured one-quarter of FP yield variability. With satellite data, both calibrated and uncalibrated yields captured FP yield variability on pure stand plots similarly well, and both captured half of FP yield variability on pure stand plots above 0.10 hectare. The uncalibrated yields were consistently 1 ton per hectare higher than FP or CC yields, and the satellite-based yields were less well correlated with the ground-based measures on intercropped plots compared with pure stand ones. Importantly, regressions using CC, FP and remotely-sensed yields as dependent variables all produced very similar coefficients for yield response to production factors.

Globally, demand for food animal products is rising. At the same time, we face mounting, related pressures including limited natural resources, negative environmental externalities, climate disruption, and population growth. Governments and other stakeholders are seeking strategies to boost food production efficiency and food system resiliency, and aquaculture (farmed seafood) is commonly viewed as having a major role in improving global food security based on longstanding measures of animal production efficiency. The most widely used measurement is called the 'feed conversion ratio' (FCR), which is the weight of feed administered over the lifetime of an animal divided by weight gained. By this measure, fed aquaculture and chickens are similarly efficient at converting feed into animal biomass, and both are more efficient compared to pigs and cattle. FCR does not account for differences in feed content, edible portion of an animal, or nutritional quality of the final product. Given these limitations, we searched the literature for alternative efficiency measures and identified 'nutrient retention', which can be used to compare protein and calories in feed (inputs) and edible portions of animals (outputs). Protein and calorie retention have not been calculated for most aquaculture species. Focusing on commercial production, we collected data on feed composition, feed conversion ratios, edible portions (i.e. yield), and nutritional content of edible flesh for nine aquatic and three terrestrial farmed animal species. We estimate that 19% of protein and 10% of calories in feed for aquatic species are ultimately made available in the human food supply, with significant variation between species. Comparing all terrestrial and aquatic animals in the study, chickens are most efficient using these measures, followed by Atlantic salmon. Despite lower FCRs in aquaculture, protein and calorie retention for aquaculture production is comparable to livestock production. This is, in part, due to farmed fish and shrimp requiring higher levels of protein and calories in feed compared to chickens, pigs, and cattle. Strategies to address global food security should consider these alternative efficiency measures.

Food retailers and manufacturers are increasingly committing to address agricultural sustainability issues in their supply chains. In place of using established eco-certifications, many companies define their own supply chain sustainability standards. Scholars remain divided on whether we should expect such company-led programs to affect change. We use a major food retailer as a critical case to evaluate the effectiveness of a company-led supply chain standard in improving environmental farm management practices. We find that the company-led standard increases the adoption of most environmental best management practices among the company's fruit, vegetable and flower growers in South Africa. This result is robust across two identification strategies: a panel analysis of over 950 farm audits and a cross-sectional matching analysis using original survey data. In-depth interviews suggest that the program's unique focus on capacity building through audit visits by highly trained staff, coupled with a close business relationship between the retailer and their growers help to explain the increased effectiveness of the program as compared to other private environmental standards. Contrary to the argument that company-led initiatives are mere window dressing, this study provides a critical example of the positive role private governance mechanisms can play in improving environmental farm management practices globally.

A critical question for agricultural production and food security is how water demand for staple crops will respond to climate and carbon dioxide (CO2) changes1, especially in light of the expected increases in extreme heat exposure2. To quantify the trade-offs between the effects of climate and CO2 on water demand, we use a ‘sink-strength’ model of demand3,4 which relies on the vapour-pressure deficit (VPD), incident radiation and the efficiencies of canopy-radiation use and canopy transpiration; the latter two are both dependent on CO2. This model is applied to a global data set of gridded monthly weather data over the cropping regions of maize, soybean, wheat and rice during the years 1948–2013. We find that this approach agrees well with Penman–Monteith potential evapotranspiration (PM) for the C3 crops of soybean, wheat and rice, where the competing CO2 effects largely cancel each other out, but that water demand in maize is significantly overstated by a demand measure that does not include CO2, such as the PM. We find the largest changes in wheat, for which water demand has increased since 1981 over 86% of the global cropping area and by 2.3–3.6 percentage points per decade in different regions.

Elevated atmospheric CO2 concentrations ([CO2]) are expected to increase C3 crop yield through the CO2 fertilization effect (CFE) by stimulating photosynthesis and by reducing stomatal conductance and transpiration. The latter effect is widely believed to lead to greater benefits in dry rather than wet conditions, although some recent experimental evidence challenges this view. Here we used a process-based crop model, the Agricultural Production Systems sIMulator (APSIM), to quantify the contemporary and future CFE on soybean in one of its primary production area of the US Midwest. APSIM accurately reproduced experimental data from the Soybean Free-Air CO2 Enrichment site showing that the CFE declined with increasing drought stress. This resulted from greater radiation use efficiency (RUE) and above-ground biomass production at elevated [CO2] that outpaced gains in transpiration efficiency (TE). Using an ensemble of eight climate model projections, we found that drought frequency in the US Midwest is projected to increase from once every 5 years currently to once every other year by 2050. In addition to directly driving yield loss, greater drought also significantly limited the benefit from rising [CO2]. This study provides a link between localized experiments and regional-scale modeling to highlight that increased drought frequency and severity pose a formidable challenge to maintaining soybean yield progress that is not offset by rising [CO2] as previously anticipated. Evaluating the relative sensitivity of RUE and TE to elevated [CO2] will be an important target for future modeling and experimental studies of climate change impacts and adaptation in C3 crops.