Attainment and maintenance of pubertal cyclicity may predict reproductive longevity in beef heifers

We hypothesized the manner that heifers achieve puberty may indicate their future reproductive longevity. Heifers with discontinued or delayed cyclicity during puberty attainment may have irregular reproductive cycles, anovulation, and infertility in their first breeding season contributing to a shorter reproductive lifespan. Therefore, plasma progesterone (P4) was measured from weaning to breeding on 611 heifers born 2012–2017 and four pubertal classifications were identified: (1) Early; P4 ≥ 1 ng/ml \u3c March 12 with continued cyclicity, (2) Typical; P4 ≥ 1 ng/ml ≥ March 12 with continued cyclicity, (3) Start-Stop; P4 ≥ 1 ng/ml but discontinued cyclicity, and (4) Non-Cycling; no P4 ≥ 1 ng/ml. Historical herd records indicated that 25% of heifers achieved puberty prior to March 12th in the 10 years prior to the study. Start-Stop and Non-Cycling yearling heifers were lighter indicating reduced growth and reproductive maturity traits compared with Early/Typical heifers. In addition, Non-Cycling/Start-Stop heifers were less responsive to prostaglandin F2 alpha (PGF2α) to initiate estrous behavior and ovulation to be artificially inseminated. Non-Cycling heifers had fewer reproductive tract score-5 and reduced numbers of calves born in the first 21-days-ofcalving during their first breeding season. Within the Start-Stop classification, 50% of heifers reinitiated cyclicity with growth traits and reproductive parameters that were similar to heifers in the Early/Typical classification while those that remained non-cyclic were more similar to heifers in the Non-Cycling group. Thus, heifers with discontinued cyclicity or no cyclicity during puberty attainment had delayed reproductive maturity resulting in subfertility and potentially a shorter reproductive lifespan

Active-Optical Reflectance Sensing Corn Algorithms Evaluated over the United States Midwest Corn Belt

Uncertainty exists with corn (Zea mays L.) N management due to year-to-year variation in crop N need, soil N supply, and N loss from leaching, volatilization, and denitrification. Activeoptical reflectance sensing (AORS) has proven effective in some fields for generating N fertilizer recommendations that improve N use efficiency, but locally derived (e.g., within a US state) AORS algorithms have not been tested simultaneously across a broad region. The objective of this research was to evaluate locally developed AORS algorithms across the US Midwest Corn Belt region for making in-season corn N recommendations. Forty-nine N response trials were conducted across eight states and three growing seasons. Reflectance measurements were collected and sidedress N rates (45–270 kg N ha–1 on 45 kg ha–1 increments) applied at approximately V9 corn development stage. Nitrogen recommendation rates from AORS algorithms were compared with the end-of-season calculated economic optimal N rate (EONR). No algorithm was within 34 kg N ha–1 of EONR \u3e 50% of the time. Average recommendations differed from EONR 81 to 147 kg N ha–1 with no N applied at planting and 74 to 118 kg N ha–1 with 45 kg of N ha–1 at planting, indicating algorithms performed worse with no N applied at planting. With some algorithms, utilizing red edge instead of the red reflectance improved N recommendations. Results demonstrate AORS algorithms developed under a particular set of conditions may not, at least without modification, perform very well in regions outside those within which they were developed

Improving an Active-Optical Reflectance Sensor Algorithm Using Soil and Weather Information

Active-optical reflectance sensors (AORS) use light reflectance characteristics from a crop canopy as an indicator of the plant’s N health. However, studies have shown AORS algorithms used in conjunction with measured reflectance characteristics for corn (Zea mays L.) N fertilizer rate recommendations are not consistently accurate. Our objective was to determine if soil and weather information could be utilized with an AORS algorithm developed at the University of Missouri (ALGMU) to improve in-season (~V9 corn development stage) N fertilizer recommendations. Nitrogen response trials were conducted across eight states over three growing seasons, totaling 49 sites with soils ranging in productivity. Nitrogen fertilizer rates according to the ALGMU were compared to economic optimal nitrogen rate (EONR). Without soil and weather information included, the root mean square error (RMSE) of the difference between ALGMU and EONR (MUDIFF) was 81 and 74 kg N ha–1 for treatments receiving 0 and 45 kg N ha–1 applied at planting, respectively. When ALGMU was adjusted using weather (seasonal precipitation and distribution prior to sidedress) and soil clay content, the RMSE was reduced by 24 to 26 kg N ha–1. Without adjustment, 20 and 29% of sites were within 34 kg N ha–1 of EONR with 0 and 45 kg N ha–1 at planting, respectively. But with adjustment for soil and weather data, 45 and 51% of sites were within 34 kg N ha–1 of EONR. These results show that weather and soil information could be used to improve ALGMU N recommendation performance

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest

Limited Sampling Strategy to Predict AUC of the CYP3A Phenotyping Probe Midazolam in Adults: Application to Various Assay Techniques

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97211/1/00912700222011418.pd

High-Input Management Systems Effect on Soybean Seed Yield, Yield Components, and Economic Break-Even Probabilities

Elevated soybean [Glycine max (L.) Merr.] prices have spurred interest in maximizing soybean seed yield and has led growers to increase the number of inputs in their production systems. However, little information exists about the effects of high-input management on soybean yield and profitability. The purpose of this study was to investigate the effects of individual inputs, as well as combinations of inputs marketed to protect or increase soybean seed yield, yield components, and economic break-even probabilities. Studies were established in nine states and three soybean growing regions (North, Central, and South) between 2012 and 2014. In each site-year both individual inputs and combination high-input (SOYA) management systems were tested. When averaged between 2012 and 2014, regional results showed no seed yield responses in the South region, but multiple inputs affected seed yield in the North region. In general, the combination SOYA inputs resulted in the greatest yield increases (up to 12%) compared to standard management, but Bayesian economic analysis indicated SOYA had low break-even probabilities. Foliar insecticide had the greatest break-even probabilities across all environments, although insect pressure was generally low across all site-years. Soybean producers in North region are likely to realize a greater response from increased inputs, but producers across all regions should carefully evaluate adding inputs to their soybean management systems and ensure that they continue to follow the principles of integrated pest management

Improving an Active-Optical Reflectance Sensor Algorithm Using Soil and Weather Information

Active-optical reflectance sensors (AORS) use light reflectance characteristics from a crop canopy as an indicator of the plant’s N health. However, studies have shown AORS algorithms used in conjunction with measured reflectance characteristics for corn (Zea maysL.) N fertilizer rate recommendations are not consistently accurate. Our objective was to determine if soil and weather information could be utilized with an AORS algorithm developed at the University of Missouri (ALGMU) to improve in-season (∼V9 corn development stage) N fertilizer recommendations. Nitrogen response trials were conducted across eight states over three growing seasons, totaling 49 sites with soils ranging in productivity. Nitrogen fertilizer rates according to the ALGMU were compared to economic optimal nitrogen rate (EONR). Without soil and weather information included, the root mean square error (RMSE) of the difference between ALGMU and EONR (MUDIFF) was 81 and 74 kg N ha–1 for treatments receiving 0 and 45 kg N ha–1 applied at planting, respectively. When ALGMU was adjusted using weather (seasonal precipitation and distribution prior to sidedress) and soil clay content, the RMSE was reduced by 24 to 26 kg N ha–1. Without adjustment, 20 and 29% of sites were within 34 kg N ha–1 of EONR with 0 and 45 kg N ha–1 at planting, respectively. But with adjustment for soil and weather data, 45 and 51% of sites were within 34 kg N ha–1 of EONR. These results show that weather and soil information could be used to improve ALGMU N recommendation performance

Incorporating lessons from high-input research into a low-margin year

Increased soybean commodity prices in recent years have generated interest in developing high-input systems to increase yield. However, little information exists about the effects of input-intensive, high-yield management on soybean yield and profitability, as well as interactions with basic agronomic practices

Active-Optical Reflectance Sensing Corn Algorithms Evaluated over the United States Midwest Corn Belt

Uncertainty exists with corn (Zea mays L.) N management due to year-to-year variation in crop N need, soil N supply, and N loss from leaching, volatilization, and denitrification. Active-optical reflectance sensing (AORS) has proven effective in some fields for generating N fertilizer recommendations that improve N use efficiency, but locally derived (e.g., within a US state) AORS algorithms have not been tested simultaneously across a broad region. The objective of this research was to evaluate locally developed AORS algorithms across the US Midwest Corn Belt region for making in-season corn N recommendations. Forty-nine N response trials were conducted across eight states and three growing seasons. Reflectance measurements were collected and sidedress N rates (45–270 kg N ha–1 on 45 kg ha–1increments) applied at approximately V9 corn development stage. Nitrogen recommendation rates from AORS algorithms were compared with the end-of-season calculated economic optimal N rate (EONR). No algorithm was within 34 kg N ha–1 of EONR \u3e 50% of the time. Average recommendations differed from EONR 81 to 147 kg N ha–1 with no N applied at planting and 74 to 118 kg N ha–1 with 45 kg of N ha–1 at planting, indicating algorithms performed worse with no N applied at planting. With some algorithms, utilizing red edge instead of the red reflectance improved N recommendations. Results demonstrate AORS algorithms developed under a particular set of conditions may not, at least without modification, perform very well in regions outside those within which they were developed