Crop breeders are looking for tools to facilitate the screening of genotypes in field trials. Remote sensing-based indices such as normalized difference vegetative index (NDVI) are sensitive to biomass and nitrogen (N) variability in crop canopies. The objectives of this study were (i) to determine if proximal sensor-based NDVI readings can differentiate the yield of winter wheat (Triticum aestivum L.) genotypes and (ii) to determine if NDVI readings can be used to classify wheat genotypes into grain yield productivity classes. This study was conducted in northeastern Colorado in 2010 and 2011. The NDVI readings were acquired weekly from March to June, during 2010 and 2011. The correlation between NDVI and grain yield was determined using Pearson’s product-moment correlation coefficient (r). The k-means clustering method was used to classify mean NDVI and mean grain yield into three classes. The overall accuracy between NDVI and yield classes was reported. The findings of this study show that, under dryland conditions, there is a reliable correlation between grain yield and NDVI at the early growing season, at the anthesis growth stage, and the mid-grain filling growth stage, as well as a poor association under irrigated conditions. Our results suggest that when the sensor is not saturated, i.e., NDVI < 0.9, NDVI could assess grain yield with fair accuracy. This study demonstrated the potential of using NDVI readings as a tool to differentiate and identify superior wheat genotypes.

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Successful precision agriculture decision making requires characterizing soil heterogeneity at high spatiotemporal resolution in real-time in order to optimize input (such as water and nutrient) amounts and location. In order to achieve this goal, a printed soil moisture sensor fabricated from biodegradable materials is demonstrated. These devices are intended to function during the growing season and then harmlessly degrade afterward, enabling high-density deployment, eliminating the need for sensor retrieval, and enabling the use of simple device structures and low-cost materials and fabrication techniques. A capacitive structure is used with a water-soluble zinc electrode printed onto a biodegradable substrate. Rapidly degrading substrate and electrode are encapsulated in a slowly degrading wax blend that protects the device, reduces drift, and controls degradation time. A linear capacitance response is observed for soil samples with a volumetric water content from 0 to 72%. Accelerated degradation testing demonstrates that the sensor responds predictably and stably until the encapsulation is breached, at which point the sensor fails rapidly, providing a clear distinction between the functional and nonfunctional lifetimes of the sensor. These results demonstrate the potential of biodegradable sensors to allow maintenance-free, affordable, and real-time soil moisture measurement at high spatial density for precision irrigation control.

Generally, improvement in the soil health of pasturelands can result in amplified ecosystem services which can help improve the overall sustainability of the system. The extent to which specific best management practices have this effect has yet to be established. A farm-scale study was conducted in eight beef-pastures in the Southern Piedmont of Georgia, from 2015 to 2018, to assess the effect of strategic-grazing (STR) and continuous-grazing hay distribution (CHD) on soil health indicators and runoff nitrate losses. In 2016, four pastures were converted to the STR system and four were grazed using the CHD system. Post-treatment, in 2018, the STR system had significantly greater POXC (by 87.1, 63.4, and 55.6 mg ha−1 at 0–5, 5–10, and 10–20 cm, respectively) as compared to CHD system. Soil respiration was also greater in the STR system (by 235 mg CO2 m-2 24 h−1) and less nitrate was lost in the runoff (by 0.21 kg ha−1) as compared to the CHD system. Cattle exclusion and overseeding vulnerable areas of pastures in STR pastures facilitated nitrogen mineralization and uptake. Our results showed that the STR grazing system could improve the sustainability of grazing systems by storing more labile carbon, efficiently mineralizing soil nitrogen, and lowering runoff nitrate losses.

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