Low-intensity tillage has become more popular among farmers in the United States and many other regions. However, accurate data on when and where low-intensity tillage methods are being used remain scarce, and this scarcity impedes understanding of the factors affecting the adoption and the agronomic or environmental impacts of these practices. In this study, we used composites of satellite imagery from Landsat 5, 7, and 8, and Sentinel-1 in combination with producer data from about 5900 georeferenced fields to train a random forest classifier and generate annual large-scale maps of tillage intensity from 2005 to 2016. We tested different combinations of hyper-parameters using cross-validation, splitting the training and testing data alternatively by field, year, and state to assess the influence of clustering on validation results and evaluate the generalizability of the classification model. We found that the best model was able to map tillage practices across the entire North Central US region at 30 m-resolution with accuracies spanning between 75% and 79%, depending on the validation approach. We also found that although Sentinel-1 provides an independent measure that should be sensitive to surface moisture and roughness, it currently adds relatively little to classification performance beyond what is possible with Landsat. When aggregated to the state level, the satellite estimates of percentage low- and high-intensity tillage agreed well with a USDA survey on tillage practices in 2006 (R2 = 0.55). The satellite data also revealed clear increases in low-intensity tillage area for most counties in the past decade. Overall, the ability to accurately map spatial and temporal patterns in tillage should facilitate further study of this important practice in the United States, as well as other regions with fewer survey-based estimates.

Aquaculture in many countries around the world has become the biggest source of seafood for human consumption. While it alleviates the pressure on wild capture fisheries, the long-term impacts of large-scale, intensive aquaculture on natural coastal systems need to be better understood. In particular, aquaculture may alter habitat and exceed the carrying capacity of coastal marine ecosystems. In this paper, we develop a high-resolution numerical model for Sanggou Bay, one of the largest kelp and shellfish aquaculture sites in Northern China, to investigate the effects of aquaculture on nutrient transport and residence time in the bay. Drag from aquaculture is parameterized for surface infrastructure, kelp canopies, and bivalve cages. A model for dissolved inorganic nitrogen (DIN) includes transport, vertical turbulent mixing, sediment and bivalve sources, and a sink due to kelp uptake. Test cases show that, due to drag from the dense aquaculture and thus a reduction of horizontal transport, kelp production is limited because DIN from the Yellow Sea is consumed before reaching the interior of the kelp farms. Aquaculture drag also causes an increase in the nutrient residence time from an average of 5 to 10 days in the middle of Sanggou Bay, and from 25 to 40 days in the shallow inner bay. Low exchange rates and a lack of DIN uptake by kelp make these regions more susceptible to phytoplankton blooms due to high nutrient retention. The risk is further increased when DIN concentrations rise due to river inflows.

Integrated assessment models generate climate change mitigation scenarios consistent with global temperature targets. To limit warming to 2 °C, cost-effective mitigation pathways rely on extensive deployments of CO2 removal (CDR) technologies, including multi-gigatonne yearly CDR from the atmosphere through bioenergy with carbon capture and storage (BECCS) and afforestation/reforestation. While these assumed CDR deployments keep ambitious temperature targets in reach, the associated rates of land-use transformation have not been evaluated. Here, we view implied integrated-assessment-model land-use conversion rates within a historical context. In scenarios with a likely chance of limiting warming to 2 °C in 2100, the rate of energy cropland expansion supporting BECCS proceeds at a median rate of 8.8 Mha yr−1 and 8.4% yr−1. This rate exceeds—by more than threefold—the observed expansion of soybean, the most rapidly expanding commodity crop. In some cases, mitigation scenarios include abrupt reversal of deforestation, paired with massive afforestation/reforestation. Historical land-use transformation rates do not represent an upper bound for future transformation rates. However, their stark contrast with modelled BECCS deployment rates implies challenges to explore in harnessing—or presuming the ready availability of—large-scale biomass-based CDR in the decades ahead. Reducing BECCS deployment to remain within these historical expansion rates would mean either the 2 °C target is missed or additional mitigation would need to occur elsewhere.

Large and regular seasonal price fluctuations in local grain markets appear to offer African farmers substantial inter-temporal arbitrage opportunities, but these opportunities remain largely unexploited: small-scale farmers are commonly observed to "sell low and buy high" rather than the reverse. In a field experiment in Kenya, we show that credit market imperfections limit farmers' abilities to move grain inter-temporally. Providing timely access to credit allows farmers to buy at lower prices and sell at higher prices, increasing farm revenues and generating a return on investment of 28%. To understand general equilibrium effects of these changes in behavior, we vary the density of loan offers across locations. We document significant effects of the credit intervention on seasonal price fluctuations in local grain markets, and show that these GE effects shape individual level profitability estimates. In contrast to existing experimental work, the results indicate a setting in which microcredit can improve firm profitability, and suggest that GE effects can substantially shape microcredit's effectiveness. In particular, failure to consider these GE effects could lead to underestimates of the social welfare benefits of microcredit interventions.