Agriculture

Where soil differs with depth, models that carry water-holding properties inferred at the surface down the whole column bias the moisture reaching crop roots. We applied depth-varying properties to a hyper-resolution land surface model resolving individual agricultural fields at 30 meters. Against ground measurements, systematic surface soil moisture biases dropped by about 14%, with error spread and timing unchanged, correcting the mean state rather than daily dynamics.

Many countries report crop yields only for large administrative units, so models trained on coarse records must be applied at finer scales, where few samples and scale-dependent relationships degrade accuracy. A machine learning model trained on field-level soybean yields in the US Midwest was fine-tuned with Brazilian state-by-year records, a technique called transfer learning, then used to predict municipal yields from 2001 to 2021. Transfer across scales rather than across regions had rarely been tested. Without any municipal training data, explained yield variance rose from 0.29 to 0.44, though transfer added little where fine-scale data were plentiful.

Much of what is known about smallholder farming rests on farmers’ own knowledge, and gaps between them and instruments used for measurements are usually treated as error. Reported field size, harvest and season timing from 640 maize farms in Zambia were checked against GPS boundaries, weighed harvests and satellite rainfall. The discrepancies were structured rather than random: small fields were overstated and large ones understated, while low harvests were understated and high ones overstated. Read instead as conceptual mismatch, they point to farmers and researchers holding different mental models.

Aflatoxin and fumonisin contamination of corn costs the United States up to an estimated $1.66 billion a year, yet the forecasting models used elsewhere have no US counterpart. Machine learning models were trained on 1,772 mycotoxin measurements from Illinois corn between 2003 and 2021, paired with daily rather than monthly weather, satellite greenness and county soil properties, which sets them apart from an earlier attempt in the state. High contamination events were rare. Even so, the best models reached 96% and 92% balanced accuracy on withheld data. Carbonate-rich soils tracked lower aflatoxin and wetter soils higher fumonisin, relationships that remain correlational.

Attributing a smallholder harvest to the farmer’s decisions rather than to soil and weather has been difficult, since field records are scarce. A process-based crop model simulated maize yields across all 72 of Zambia’s districts from 1979 to 2016, varying cultivar, fertilizer rate and planting date, which resolves those three choices separately for the first time nationally. Management explained 53% of yield variance overall, from 27% in the drier south to 82% in the wetter north, where fertilizer alone accounted for 72%.

While potential maize yields in Zambia are 9 t per hectare (t/ha), the average farmer produces only 1–2. We evaluate the contribution of responses to weather risk to that gap by decomposing the yield gap in maize in Zambia. Risk aversion explained 24% of the average gap, varying widely by location, implying 6.75 tons per hectare attainable without added risk against 8.84 when maximizing profit.

Soil water is a major uncertainty in crop models, and correcting it against measured soil moisture can reduce uncertainty on yield gain estimation. We tested 19 site-years of corn and soybean at five US Midwest sites, 2011 to 2019, and tested such corrections using in-situ sensors and satellite soil moisture, including SMAP-HydroBlocks, a 30-meter product not previously used in a crop model. Sensors improved yield estimates in 63% of site-years, mostly under water stress. The satellite data constrained the soil profile far more weakly yet still cut yield error by a median 17.2%.

Integrating computational models and hydroclimate data for actionable agriculture decision-making. Towards resilience and adaptability in a changing climate.

Soil moisture is a direct measure of agricultural drought, yet operational global indices rely on rainfall or model estimates rather than observed soil water in near-real time. We developed the first operational global soil moisture index from merged near-real-time satellite data, mapping daily percentiles at a 7-8 day lag. Over the Canadian Prairies it tracked the dry 2012-2013, wet 2016 and severe 2017 drought.