Crop rotational diversity can mitigate climate-induced grain yield losses.

Diversified crop rotations have been suggested to reduce grain yield losses from the adverse climatic conditions increasingly common under climate change. Nevertheless, the potential for climate change adaptation of different crop rotational diversity (CRD) remains undetermined. We quantified how climatic conditions affect small grain and maize yields under different CRDs in 32 long-term (10-63 years) field experiments across Europe and North America. Species-diverse and functionally rich rotations more than compensated yield losses from anomalous warm conditions, long and warm dry spells, as well as from anomalous wet (for small grains) or dry (for maize) conditions. Adding a single functional group or crop species to monocultures counteracted yield losses from substantial changes in climatic conditions. The benefits of a further increase in CRD are comparable with those of improved climatic conditions. For instance, the maize yield benefits of adding three crop species to monocultures under detrimental climatic conditions exceeded the average yield of monocultures by up to 553 kg/ha under non-detrimental climatic conditions. Increased crop functional richness improved yields under high temperature, irrespective of precipitation. Conversely, yield benefits peaked at between two and four crop species in the rotation, depending on climatic conditions and crop, and declined at higher species diversity. Thus, crop species diversity could be adjusted to maximize yield benefits. Diversifying rotations with functionally distinct crops is an adaptation of cropping systems to global warming and changes in precipitation.

Study on the prediction method of grasshopper occurrence risk in Inner Mongolia based on the maximum entropy model during the growing period.

Grasshoppers represent a significant biological challenge in Inner Mongolia's grasslands, severely affecting the region's animal husbandry. Thus, dynamic monitoring of grasshopper infestation risk is crucial for sustainable livestock farming. This study employed the Maxent model, along with remote sensing data, to forecast Oedaleus decorus asiaticus occurrence during the growing season, using grasshopper suitability habitats as a base. The Maxent model's predictive accuracy was high, with an AUC of 0.966. The most influential environmental variables for grasshopper distribution were suitable habitat data (34.27%), the temperature-vegetation dryness index during the spawning period (18.81%), and various other meteorological and vegetation factors. The risk index model was applied to calculate the grasshopper distribution across different risk levels for the years 2019-2022. The data indicated that the level 1 risk area primarily spans central, eastern, and southwestern Inner Mongolia. By examining the variable weights, the primary drivers of risk level fluctuation from 2019 to 2022 were identified as accumulated precipitation and land surface temperature anomalies during the overwintering period. This study offers valuable insights for future O. decorus asiaticus monitoring in Inner Mongolia.

In-season weather data provide reliable yield estimates of maize and soybean in the US central Corn Belt.

Weather conditions regulate the growth and yield of crops, especially in rain-fed agricultural systems. This study evaluated the use and relative importance of readily available weather data to develop yield estimation models for maize and soybean in the US central Corn Belt. Total rainfall (Rain), average air temperature (Tavg), and the difference between maximum and minimum air temperature (Tdiff) at weekly, biweekly, and monthly timescales from May to August were used to estimate county-level maize and soybean grain yields for Iowa, Illinois, Indiana, and Minnesota. Step-wise multiple linear regression (MLR), general additive (GAM), and support vector machine (SVM) models were trained with Rain, Tavg, and with/without Tdiff. For the total study area and at individual state level, SVM outperformed other models at all temporal levels for both maize and soybean. For maize, Tavg and Tdiff during July and August, and Rain during June and July, were relatively more important whereas for soybean, Tavg in June and Tdiff and Rain during August were more important. The SVM model with weekly Rain and Tavg estimated the overall maize yield with a root mean square error (RMSE) of 591 kg ha-1 (4.9% nRMSE) and soybean yield with a RMSE of 205 kg ha-1 (5.5% nRMSE). Inclusion of Tdiff in the model considerably improved yield estimation for both crops; however, the magnitude of improvement varied with the model and temporal level of weather data. This study shows the relative importance of weather variables and reliable yield estimation of maize and soybean from readily available weather data to develop a decision support tool in the US central Corn Belt.

Towards sustainable maize production in the U.S. upper Midwest with interseeded cover crops.

The incorporation of cover crops into the maize (Zea mays L.)-soybean [Glycine max (L.) Merr.] rotation in the U.S. upper Midwest may improve sustainability. Long, cold winters in the region make identifying successful cover crop species and management practices a challenge. Two experiments were conducted in Minnesota, USA from fall 2016 through spring 2019 to examine the effect of cover crops interseeded at four- to six-leaf collar (early-interseeded) and dent to physiological maturity (late-interseeded) on biomass and grain yield of maize. Annual ryegrass (Lolium multiflorum L.) and cereal rye (Secale cereale L.) were evaluated as monocultures and in mixtures with crimson clover (Trifolium incarnatum L.) and forage radish (Raphanus sativus L.). Differences in canopy cover and biomass of late-interseeded cover crops were observed at the southernmost location in 2018. Additional accumulated growing-degree days in fall 2018 did not translate into increased cover crop canopy coverage of late-interseeded cover crops. Differences in cover crop canopy cover and biomass of early-interseeded cover crops were observed by fall frost at all locations in 2017 and at the northernmost location in 2018. Cover crop canopy cover and biomass at termination before planting maize, soil moisture at maize planting as well as maize aboveground biomass and yield were not affected by spring cereal rye regrowth of cover crops late-interseeded the previous year. Similarly, early-interseeded cover crops did not affect maize aboveground biomass or yield. We attribute these results to limited cover crop growth. This highlights the potential of a variety of cover crop strategies interseeded into maize in the U.S. upper Midwest; however, efforts to fine-tuning cover crop management and weather conditions are needed to benefit from such practice.