Effect of Micro-Aid\u3csup\u3e®\u3c/sup\u3e Supplementation on Nitrogen Losses from Manure

A 2x2 factorial designed experiment was used to study the effects of Micro-Aid and time on OM and N losses from manure, in a simulated feedlot pen setting. Manure was collected from cattle on a common diet, except for the addition of 1 g Micro-Aid /steer daily. Losses of OM were greater at 60 d than 30 d, and greater for control than Micro-Aid. Nitrogen losses at d 30 were similar between treatments but control pans had greater N losses at d 60. Feeding Micro-Aid to cattle may inhibit N volatilization from manure, enhancing the fertilizer value of manure

Effect of Micro-Aid\u3csup\u3e®\u3c/sup\u3e Supplementation on Nitrogen Losses from Manure

A 2x2 factorial designed experiment was used to study the effects of Micro-Aid and time on OM and N losses from manure, in a simulated feedlot pen setting. Manure was collected from cattle on a common diet, except for the addition of 1 g Micro-Aid /steer daily. Losses of OM were greater at 60 d than 30 d, and greater for control than Micro-Aid. Nitrogen losses at d 30 were similar between treatments but control pans had greater N losses at d 60. Feeding Micro-Aid to cattle may inhibit N volatilization from manure, enhancing the fertilizer value of manure

Machine Learning Approach for Prescriptive Plant Breeding

We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding

Yield and dry matter productivity of Japanese and US soybean cultivars

The difference in yields of cultivars may be causing difference in soybean yield between Japan and the USA. The objective of this study was to identify the effect of the cultivar on dry matter production and to reveal the key factors causing the differences in yield by focusing utilization of solar radiation in recent Japanese and US soybean cultivars. Field experiments were conducted during two seasons in Takatsuki, Japan (34°50′), and in a single season in Fayetteville (36°04′), AR, USA. Five Japanese and 10 US cultivars were observed under near-optimal conditions in order to achieve yields as close to their physiological potential as possible. The seed yield and total aboveground dry matter (TDM) were measured at maturity as long as radiation was intercepted by the canopy. The seed yield ranged from 3.10t ha−1 to 5.91t ha−1. Throughout the three environments, the seed yield of US cultivars was significantly higher than that of Japanese cultivars. The seed yield correlated with the TDM rather than the HI with correlation coefficients from .519 to .928 for the TDM vs. .175 to .800 for the HI, for each of the three environments. The higher TDM of US cultivars was caused by a higher radiation use efficiency rather than higher total intercepted radiation throughout the three environments. The seasonal change in the TDM observed in four cultivars indicated that dry matter productivity was different between cultivars, specifically during the seed-filling period