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

Soil sample timing, nitrogen fertilization, and incubation length influence anaerobic potentially mineralizable nitrogen

Understanding the variables that affect the anaerobic potentially mineralizable N (PMNan) test should lead to a standard procedure of sample collection and incubation length, improving PMNan as a tool in corn (Zea mays L.) N management. We evaluated the effect of soil sample timing (preplant and V5 corn development stage [V5]), N fertilization (0 and 180 kg ha−1) and incubation length (7, 14, and 28 d) on PMNan (0–30 cm) across a range of soil properties and weather conditions. Soil sample timing, N fertilization, and incubation length affected PMNan differently based on soil and weather conditions. Preplant vs. V5 PMNan tended to be greater at sites that received \u3c 183 mm of precipitation or \u3c 359 growing degree-days (GDD) between preplant and V5, or had soil C/N ratios \u3e 9.7:1; otherwise, V5 PMNan tended to be greater than preplant PMNan. The PMNan tended to be greater in unfertilized vs. fertilized soil in sites with clay content \u3e 9.5%, total C \u3c 24.2 g kg−1, soil organic matter (SOM) \u3c 3.9 g kg−1, or C to N ratios \u3c 11.0:1; otherwise, PMNan tended to be greater in fertilized vs. unfertilized soil. Longer incubation lengths increased PMNan at all sites regardless of sampling methods. Since PMNan is sensitive to many factors (sample timing, N fertilization, incubation length, soil properties, and weather conditions), it is important to follow a consistent protocol to compare PMNan among sites and potentially use PMNan to improve corn N management

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 \u3c 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions

United States Midwest Soil and Weather Conditions Influence Anaerobic Potentially Mineralizable Nitrogen

Nitrogen provided to crops through mineralization is an important factor in N management guidelines. Understanding of the interactive effects of soil and weather conditions on N mineralization needs to be improved. Relationships between anaerobic potentially mineralizable N (PMNan) and soil and weather conditions were evaluated under the contrasting climates of eight US Midwestern states. Soil was sampled (0–30 cm) for PMNan analysis before pre-plant N application (PP0N) and at the V5 development stage from the pre-plant 0 (V50N) and 180 kg N ha−1 (V5180N) rates and incubated for 7, 14, and 28 d. Even distribution of precipitation and warmer temperatures before soil sampling and greater soil organic matter (SOM) increased PMNan. Soil properties, including total C, SOM, and total N, had the strongest relationships with PMNan (R2 ≤ 0.40), followed by temperature (R2 ≤ 0.20) and precipitation (R2 ≤ 0.18) variables. The strength of the relationships between soil properties and PMNan from PP0N, V50N, and V5180N varied by ≤10%. Including soil and weather in the model greatly increased PMNan predictability (R2 ≤ 0.69), demonstrating the interactive effect of soil and weather on N mineralization at different times during the growing season regardless of N fertilization. Delayed soil sampling (V50N) and sampling after fertilization (V5180N) reduced PMNan predictability. However, longer PMNan incubations improved PMNan predictability from both V5 soil samplings closer to the PMNan predictability from PP0N, indicating the potential of PMNan from longer incubations to provide improved estimates of N mineralization when N fertilizer is applied

Relating four‐day soil respiration to corn nitrogen fertilizer needs across 49 U.S. Midwest fields

Soil microbes drive biological functions that mediate chemical and physical processes necessary for plants to sustain growth. Laboratory soil respiration has been proposed as one universal soil health indicator representing these functions, potentially informing crop and soil management decisions. Research is needed to test the premise that soil respiration is helpful for profitable in‐season nitrogen (N) rate management decisions in corn (Zea mays L.). The objective of this research was two‐fold: (i) determine if the amount of N applied at the time of planting effected soil respiration, and (ii) evaluate the relationship of soil respiration to corn yield response to fertilizer N application. A total of 49 N response trials were conducted across eight states over three growing seasons (2014–2016). The 4‐day Comprehensive Assessment of Soil Health (CASH) soil respiration method was used to quantify soil respiration. Averaged over all sites, N fertilization did not impact soil respiration, but at four sites soil respiration decreased as N fertilizer rate applied at‐planting increased. Across all site‐years, soil respiration was moderately related to the economical optimum N rate (EONR) (r2 = 0.21). However, when analyzed by year, soil respiration was more strongly related to EONR in 2016 (r2 = 0.50) and poorly related for the first two years (r2 \u3c 0.20). These results illustrate the factors influencing the ability of laboratory soil respiration to estimate corn N response, including growing‐season weather, and the potential of fusing soil respiration with other soil and weather measurements for improved N fertilizer recommendations

2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease

The recommendations listed in this document are, whenever possible, evidence based. An extensive evidence review was conducted as the document was compiled through December 2008. Repeated literature searches were performed by the guideline development staff and writing committee members as new issues were considered. New clinical trials published in peer-reviewed journals and articles through December 2011 were also reviewed and incorporated when relevant. Furthermore, because of the extended development time period for this guideline, peer review comments indicated that the sections focused on imaging technologies required additional updating, which occurred during 2011. Therefore, the evidence review for the imaging sections includes published literature through December 2011