Optimizing nitrogen (N) management in rice is crucial for China’s food security and sustainable agricultural development. Nondestructive crop growth monitoring based on remote sensing technologies can accurately assess crop N status, which may be used to guide the in-season site-specific N recommendations. The fixed-wing unmanned aerial vehicle (UAV)-based remote sensing is a low-cost, easy-to-operate technology for collecting spectral reflectance imagery, an important data source for precision N management. The relationships between many vegetation indices (VIs) derived from spectral reflectance data and crop parameters are known to be nonlinear. As a result, nonlinear machine learning methods have the potential to improve the estimation accuracy. The objective of this study was to evaluate five different approaches for estimating rice (Oryza sativa L.) aboveground biomass (AGB), plant N uptake (PNU), and N nutrition index (NNI) at stem elongation (SE) and heading (HD) stages in Northeast China: (1) single VI (SVI); (2) stepwise multiple linear regression (SMLR); (3) random forest (RF); (4) support vector machine (SVM); and (5) artificial neural networks (ANN) regression. The results indicated that machine learning methods improved the NNI estimation compared to VI-SLR and SMLR methods. The RF algorithm performed the best for estimating NNI (R2 = 0.94 (SE) and 0.96 (HD) for calibration and 0.61 (SE) and 0.79 (HD) for validation). The root mean square errors (RMSEs) were 0.09, and the relative errors were <10% in all the models. It is concluded that the RF machine learning regression can significantly improve the estimation of rice N status using UAV remote sensing. The application machine learning methods offers a new opportunity to better use remote sensing data for monitoring crop growth conditions and guiding precision crop management. More studies are needed to further improve these machine learning-based models by combining both remote sensing data and other related soil, weather, and management information for applications in precision N and crop management.
Small grain cover crops offer grazing opportunities but effects on following row crops are not well understood. From 1999 through 2008, stocker steers (Bos taurus) grazed small grains in a two-paddock crop rotation of rye (Secale cereale L.), cotton (Gossypium hirsutum L.), wheat (Triticum aestivum L.), fallow, and rye. Treatments in 2005Treatments in , 2007Treatments in , and 2008 included (i) zerograzed rye from 1999; (ii) ungrazed rye in the current year only; (iii) always grazed rye; and (iv) in 2007 and 2008, monoculture cotton (since 1999) with no cover crop. The experiment, on a Pullman clay loam (fine, mixed, superactive, thermic Torrertic Paleustolls), was a randomized complete block design with three blocks. Ungrazed rye was mechanically harvested before planting cotton. Rye excluded from grazing in 1 yr only (Treatment 2) was taller (P < 0.05) and produced more forage mass than zero-grazed rye (Treatment 1), likely due to higher rye tiller numbers, weight, and basal cover. Cotton planted into grazed rye had more plants m -1 row and was taller until July (P < 0.05), and in 2005 had greater (P < 0.05) lint and seed yield than where grazing was excluded. Continuous cotton with no cover crop (Treatment 4) was shorter (P < 0.01) than cotton grown with a cover crop regardless of grazing. Known allelopathic chemicals were detected in rye and soil where rye grew and appeared to be influenced by grazing. Increased growth and productivity of rye and cotton where grazing occurred may be related to suppressive effects of grazing on allelopathy.
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