Cover crops decrease maize yield variability in sloping landscapes through increased water during reproductive stages

Abstract Efforts to understand and quantify how a changing climate can impact agriculture often rely on bias-corrected and downscaled climate information, making it important to quantify potential biases of this approach. Previous studies typically focus their uncertainty analyses on climatic variables and are silent on how these uncertainties propagate into human systems through their subsequent incorporation into econometric models. Here, we use a multi-model ensemble of statistically downscaled and bias-corrected climate models, as well as the corresponding CMIP5 parent models, to analyze uncertainty surrounding annual maize yield variability in the United States. We find that the CMIP5 models considerably overestimate historical yield variability while the bias-corrected and downscaled versions underestimate the largest historically observed yield shocks. We also find large differences in projected yields and other decision-relevant metrics throughout this century, leaving stakeholders with modeling choices that require navigating trade-offs in resolution, historical accuracy, and projection confidence.

Agricultural soils can be affected in their ecological functions by in-field traffic of agricultural machinery. A three-factorial research design was carried out in a field experiment to test the effect of slurry tanker filling level (filled, half-filled, empty), tire inflation pressure of the slurry tanker (high: 300 kPa, low: 100 kPa), and ground covering (+cover crop, −cover crop) on tire track and soil penetration resistance (averaged, 0–20 cm, 21–40 cm) after application on the fields in spring. Additionally, the effect on grain yield of the subsequent culture was considered. The total weight of the tractor slurry tanker combination was 16,470 kg (empty), 25,940 kg (half-filled), and 34,620 kg (filled). The low tire inflation pressure of the slurry tanker increased the mean tire–soil contact area by 75% (filled), 38% (half-filled), and 16% (empty tanker). The results obtained show a significant effect of tire inflation pressure and ground covering on the measured parameters. The tire inflation pressure reduction effect on track depth was highest in the filled slurry tanker (−17.8%). With increasing wheel load, the effect of reduced tire inflation pressure on soil penetration resistance (0–20 cm) increased. In the subsoil (21–40 cm), the effect of tire inflation pressure was much lower, indicating that a reduction of tire inflation pressure preserves the upper layers rather than the lower ones. Furthermore, cover crops are linked to a higher degree of soil deformation after traffic with the tractor–slurry combination due to their loosening effect on the topsoil. Tire tracks were 15.0% deeper in the cover crop field than in the field without a cover crop. It is assumed that cover crop mixtures with different types of root mass can influence the mitigation of soil compaction in an ameliorative way.

SUMMARYMultilocation experiments were established to determine the best strategy for using inorganic fertilizer in conservation agriculture (CA) systems that use green manure cover crops, namely sunhemp, velvet bean and cowpea grown in rotation with maize. The objectives of the study were to determine (i) the effect of half and full rates of basal fertilizer on maize and legume biomass yields, (ii) the residual effects of unfertilized, half and fully fertilized green manure legumes on maize grown after the legumes, and (iii) the residual effect of unfertilized, half and fully fertilized green manure legumes combined with basal and topdressing fertilizer on maize yields. Experimental design was a randomized complete block with basal fertilizer as a treatment in the green manure legumes phase. Previously, in the maize phase, green manure legume species were the main treatment with basal fertilizer as a subtreatment (sunhemp, velvet bean and cowpea: 0, 75, 150 kg ha−1and 0, 50, 100 kg ha−1, respectively). Nitrogen was applied in the maize phase at 0, 23, 46, 69 kg N ha−1as a sub-subtreatment in Malawi. Results showed that inorganic fertilizer is the most effective when applied to the maize, not green manure legumes. Biomass of green manure legumes, sunnhemp 8084 kg ha−1, velvet bean 7678 kg ha−1and cowpea 4520 kg ha−1, was not significantly affected by application of basal fertilizer. Maize production increased after the application of green manure legumes with maize-after-maize, maize-after-velvet bean, maize-after-sunnhemp and maize-after-cowpea, yielding 3804, 5440, 5446 and 5339 kg ha−1, respectively. Nitrogen increased maize yield regardless of the previously used green manure legumes species. Our results suggest that farmers should apply fertilizer to maize and grow green manure legumes on residual soil in CA systems. Despite growing green manure legumes, smallholders should apply nitrogen topdressing to maize grown using the green manure legumes in some agro-ecologies.

Abstract. JULES-crop is a parametrisation of crops in the Joint UK Land Environment Simulator (JULES). We investigate the sources of the interannual variability in the modelled maize yield, using global runs driven by reanalysis data, with a view to understanding the impact of various approximations in the driving data and initialisation. The standard forcing dataset for JULES consists of a combination of meteorological variables describing precipitation, radiation, temperature, pressure, specific humidity and wind, at subdaily time resolution. We find that the main characteristics of the modelled yield can be reproduced with a subset of these variables and using daily forcing, with internal disaggregation to the model timestep. This has implications in particular for the use of the model with seasonal forcing data, which may not have been provided at subdaily resolution for all required driving variables. We also investigate the effect on annual yield of initialising the model with climatology on the sowing date. This approximation has the potential to considerably simplify the use of the model with seasonal forecasts, since obtaining observations or reanalysis output for all the initialisation variables required by JULES for the start date of the seasonal forecast would present significant practical challenges.