Abstract
Adoption of no-till systems in Eastern Washington has been slow due to the difficulty of managing wheat (Triticum aestivum L.) straw residue and the unknown decomposition potential of cultivars. We hypothesize that by analyzing wheat straw with near-infrared spectroscopy (NIRS), calibration models can be developed to accurately predict fiber and chemical constituents of wheat, determining straw decomposition potential. Straw from a panel of 480 soft winter wheat cultivars adapted to the Pacific Northwest are analyzed for neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), cellulose, hemicellulose, carbon (C), and nitrogen (N). Using modified partial least squares regression and cross validation techniques, specific environment and broad-based NIRS models are calibrated and predictive ability is validated. R2cal values from broad models are better than the specific models, and are 0.85 (NDF), 0.86 (ADF), 0.65 (ADL), 0.88 (cellulose), 0.42 (hemicellulose), 0.67 (C), and 0.73 (N). The corresponding SEP values are 1.68% (NDF), 1.54% (ADF), 0.62% (ADL), 1.14% (cellulose), 1.11% (hemicellulose), 1.23% (C), and 0.06% (N). A Finch × Eltan breeding population is used to further validate models and prediction accuracies for variety selection within a breeding program scenario. The broad NIRS models prove useful for estimating high and low ranges of NDF, ADF, and cellulose in wheat cultivars which translate into characteristics of slow and fast decomposition potential.
Highlights
The rainfed regions of Eastern Washington are recognized for their highly successful dryland wheat (Triticum aestivum L.) production systems
NDF is neutral detergent fiber, ADF is acid detergent fiber, ADL is acid detergent lignin, CELL is cellulose, HEMI is hemicellulose, C is carbon, N is nitrogen. n is number of samples in calibration set; SD is standard deviation; Bias is the difference between the mean of reference data and the mean of Near-infrared spectroscopy (NIRS) predicted values; R2 pred is coefficient of determination; SEP is standard error of prediction
Our broad NIRS models were successful in predicting NDF, ADF, and cellulose of the first validation set with high accuracy whereas hemicellulose and ADL
Summary
The rainfed regions of Eastern Washington are recognized for their highly successful dryland wheat (Triticum aestivum L.) production systems. In regions receiving high precipitation (>300 mm), annual cropping is generally practiced with a three-year rotation of winter wheat/spring cereal/spring legume [3]. Within no-till production systems in high rainfall regions, straw will need to decompose rapidly over the winter months to avoid the planting complications that excessive straw residue can create in the spring. The use of NIRS is routine for prediction of various traits in many other agricultural crops such as postharvest quality of apples [28], postharvest ripeness and quality of mango [29,30], and carbohydrate content in zucchini fruit [31] The objective of this experiment was to analyze a diverse winter wheat population with NIRS to develop prediction models that would facilitate the determination of residue decomposition rates. The risk involved with transitioning to no-till systems will be reduced because farmers will be able to make informed decisions when selecting a cultivar that meets their specific needs for production
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