Phenological stage and vegetation index for predicting corn yield under rainfed environments.

Uncrewed aerial systems (UASs) provide high temporal and spatial resolution information for crop health monitoring and informed management decisions to improve yields. However, traditional in-season yield prediction methodologies are often inconsistent and inaccurate due to variations in soil types and environmental factors. This study aimed to identify the best phenological stage and vegetation index (VI) for estimating corn yield under rainfed conditions. Multispectral images were collected over three years (2020-2022) during the corn growing season and over fifty VIs were analyzed. In the three-year period, thirty-one VIs exhibited significant correlations (r ≥ 0.7) with yield. Sixteen VIs were significantly correlated with the yield at least for two years, and five VIs had a significant correlation with the yield for all three years. A strong correlation with yield was achieved by combining red, red edge, and near infrared-based indices. Further, combined correlation and random forest an alyses between yield and VIs led to the identification of consistent and highest predictive power VIs for corn yield prediction. Among them, leaf chlorophyll index, Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index and modified normalized difference at 705 were the most consistent predictors of corn yield when recorded around the reproductive stage (R1). This study demonstrated the dynamic nature of canopy reflectance and the importance of considering growth stages, and environmental conditions for accurate corn yield prediction.

Transitioning from conventional to cover crop systems with minimum tillage does not alter nutrient loading.

Nutrient loading from conventional row-crop production systems impairs surface waterbodies in the mid-southern United States. This study was conducted to determine whether minimum tillage and winter cover crops can decrease nutrient loading in surface runoff from conventionally tilled row-crop fields. The effects of winter cover crops and minimum tillage on N and P loading from a corn (Zea mays L.)-soybean [Glycine max (L.) Merr.] rotation system were investigated on production fields in northwestern Mississippi using a split-field approach. As measured at the edge of the field, minimum tillage with cover crops had no effect on surface runoff from production fields regarding N or P loading (p > .10 for all nutrient loads), discharge (p > .10), or loss of suspended solids (p > .10). Minimum tillage and cover crops decreased sediment and nutrient concentrations in runoff for total N (p = .05) and total P (p = .09) but had no effect on other nutrients of interest. Although these practices decreased total N concentration by 36% in surface runoff to receiving waters, this reduction was only seen when aboveground cover crop biomass was present (p = .07). Regardless of the time of year, minimum tillage with cover crops decreased total P concentration in surface runoff by 27% (p = .09). These data indicate that it is unlikely that minimum tillage and cover crops will affect N and P loading while transitioning to a conservation production system in the mid-southern United States.

Investigation of denitrifying microbial communities within an agricultural drainage system fitted with low-grade weirs.

Enhancing wetland characteristics in agricultural drainage ditches with the use of low-grade weirs, has been identified as a best management practice (BMP) to mitigate nutrient runoff from agriculture landscapes. A major objective of utilizing low-grade weirs as a BMP includes fostering environments suitable for the biogeochemical removal of nitrogen via denitrification. This study examined the spatial resolution of microbial communities involved in denitrification in agricultural drainage systems fitted with low-grade weirs. Appropriate sampling scales of microbial communities were investigated using 16S rRNA and denitrification functional genes nosZ, nirS, and nirK via quantitative polymerase chain reaction (qPCR) and terminal-restriction fragment length polymorphism (T-RFLP) analysis. Genes 16S rRNA, nosZ, and nirS were all successfully detected in soil samples, while nirK was below the detection limit throughout the study. Utilizing a combination of three sampling regimes (management, reach, catchment) was found to be effective in capturing microbial community patterns, as ANOVA results revealed nosZ gene abundance was significantly greater at the management rather than reach scale (p = 0.045; F = 3.311), although, no significant differences were observed in 16S rRNA or nirS between sampling scales (p > 0.05). A Pearson correlation matrix confirmed that 16S rRNA and nosZ gene abundances were positively correlated with soil carbon (C), nitrogen (N), and moisture, while nirS abundance was only positively correlated with soil C and soil moisture. This highlights the potential for wetland-like characteristics to be recovered in agricultural drainage systems, as weir proximity is observed to enhance soil moisture and conditions for N remediation. This study provides the basis for additional investigations of these unique environments in the Mississippi Alluvial Valley and a starting point for adaptive management to enhance agricultural drainage systems for microbial communities towards nutrient remediation goals.