Greenhouse gas emissions from soils in corn-based cropping systems.

Crop rotation is a management practice with high greenhouse gas (GHG) mitigating potential that is often neglected due to economic influences. Three long-term rotation studies in Wisconsin were selected to assess the potential opportunities for mitigating GHG emissions by comparing the temporal and spatial variability of N2 O, CO2 , and CH4 emissions in continuous corn (CC) (Zea mays L.), corn-soybean (CS) [Glycine max (L.) Merr.], and corn-soybean-wheat (CSW) (Triticum aestivum L.) using a static chamber method. GHG emissions were influenced by weather conditions and following nitrogen (N) application during a 3-year measurement period. In high N input environments at Arlington and Lancaster, N2 O emissions in CC were 5.80 and 4.40 kg N ha-1 , respectively, which was much higher than the emissions in CS and CSW rotations that ranged from 1.52 to 3.33 kg N ha-1 . In the low N input environment at Marshfield, N2 O emissions were not statistically different among CC, CS, and CSW rotations (1.20-1.66 kg N ha-1 ). Yield-scaled N2 O emissions were not different among crop rotations. When pooled over locations, CO2 emissions were highest in CC (4.16 Mg C ha-1 ) and were similar in CS and CSW (3.71 and 3.50 Mg C ha-1 , respectively). Soils either emitted or absorbed small and inconsistent amounts of CH4 . These results provide important insights as to how weather conditions and differences among management practices affect GHG emissions and show that application of either 2-year CS or 3-year CSW rotation can be equally effective in reducing N2 O emissions compared to CC, especially with high N applications.

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.

Neonicotinoid seed treatments of soybean provide negligible benefits to US farmers.

Neonicotinoids are the most widely used insecticides worldwide and are typically deployed as seed treatments (hereafter NST) in many grain and oilseed crops, including soybeans. However, there is a surprising dearth of information regarding NST effectiveness in increasing soybean seed yield, and most published data suggest weak, or inconsistent yield benefit. The US is the key soybean-producing nation worldwide and this work includes soybean yield data from 194 randomized and replicated field studies conducted specifically to evaluate the effect of NSTs on soybean seed yield at sites within 14 states from 2006 through 2017. Here we show that across the principal soybean-growing region of the country, there are negligible and management-specific yield benefits attributed to NSTs. Across the entire region, the maximum observed yield benefits due to fungicide (FST = fungicide seed treatment) + neonicotinoid use (FST + NST) reached 0.13 Mg/ha. Across the entire region, combinations of management practices affected the effectiveness of FST + NST to increase yield but benefits were minimal ranging between 0.01 to 0.22 Mg/ha. Despite widespread use, this practice appears to have little benefit for most of soybean producers; across the entire region, a partial economic analysis further showed inconsistent evidence of a break-even cost of FST or FST + NST. These results demonstrate that the current widespread prophylactic use of NST in the key soybean-producing areas of the US should be re-evaluated by producers and regulators alike.