Grain yield,nitrogen use efficiency and tillage intensity in wheat as affected by precision nutrient management

ABSTRACT

The present studies were conducted to evaluate the effect of different nutrient management practices under two tillage options in wheat. The experiments were laid out in split-plot design with a combination of two varieties (WH 1105 and HD 2967) and two tillage options (Conventional and No tillage) in the main plot and six precision nutrient management practices [absolute control, site-specific nutrient management with Nutrient Expert for wheat (SSNM-NE)(170 kg nitrogen (N)/ha), SSNM NE+GreenSeeker (GS)(153/158 N kg/ha), N₁₂₀ (120 kg N/ha) before irrigation, N₁₂₀ after irrigation and N Rich (180 kg N/ha)] in subplot replicated thrice. The grain yield and quality characters in no tillage (NT) and conventional tillage (CT) were similar but agronomic efficiency was higher in NT. Both the varieties (WH 1105 and HD 2967) gave similar grain yield and quality. Wheat variety WH 1105 recorded significantly higher sodium dodecyl sulfate sedimentation (SDS) and gluten index. The treatment SSNM NE+GS had resulted in 107.1% higher grain yield than no nitrogen control but similar to enriched N plot (180 kg N/ha). The grain protein, SDS and gluten index in need-based nutrient management (SSNM+GS) treatment were found to be similar as recorded in SSNM-NE (170 kgN/ha) and N enriched plot (180 kg N ha^?1). The agronomic efficiency and recovery efficiency in SSNM+GS were also better than SSNM NE.     

Spectral indices measured with proximal sensing using canopy reflectance sensor, chlorophyll meter and leaf color chart for in-season grain yield prediction of basmati rice

A number of optical sensing tools are now available and can potentially be used for refining need-based fertilizer nitrogen (N) topdressing decisions. Algorithms for estimating field-specific fertilizer N needs are based on predictions of yield made while the crops are still growing in the field. The present study was conducted to establish and validate yield prediction models using spectral indices measured with proximal sensing using GreenSeeker canopy reflectance sensor, soil and plant analyzer development (SPAD) chlorophyll meter, and two different types of leaf color charts (LCCs) for five basmati rice genotypes across different growth stages. Regression analysis was performed using normalized difference vegetation index (NDVI) recorded with GreenSeeker sensor and leaf greenness indices measured with SPAD meter and LCCs developed by Punjab Agricultural University, Ludhiana (India) (PAU-LCC) and the International Rice Research Institute, Philippines (IRRI-LCC). The exponential relationship between NDVI and grain yield exhibited the highest coefficient of determination (R²) and minimum normalized root mean square error (NRMSE) at panicle initiation stage and explained 38.3%-76.4% variation in yield using genotype-specific models. Spectral indices pooled for different genotypes were closely related to grain yield at all growth stages and explained 53.4%-57.2% variation in grain yield. Normalizing different spectral indices with cumulative growing degree days (CGDD) decreased the accuracy of yield prediction. Normalization with days after transplanting (DAT), however, did not reduce or improve the predictability of yield. The ability of each model to predict grain yield was validated with an independent dataset collected from two experiments conducted at different sites with a range of fertilizer N doses. The NDVI-based genotype-specific models exhibited a robust linear correlation (R²=0.77, NRMSE=7.37%, n=180) between observed and predicted grain yields only at 35 DAT (i.e., panicle initiation stage), while the SPAD, PAU-LCC, and IRRI-LCC consistently provided reliable predictions (with respective R² of 0.63, 0.60, and 0.53 and NRMSE of 10%, 10%, and 13.6%) even with genotype invariant models with 900 data points obtained at different growth stages. The study revealed that unnormalized values of spectral indices, namely NDVI, SPAD, PAU-LCC, and IRRI-LCC, can be satisfactorily used for in-season estimation of grain yield for basmati rice. As LCCs are very economical compared with chlorophyll meters and canopy reflectance sensors, they can be preferably used by small farmers in developing countries.     

主动作物冠层传感器GreenSeeker在马铃薯氮素营养诊断中应用的可行性分析

长期以来氮肥的施用在马铃薯生产中起到了重要作用。但是,随着氮肥施用量的增加,氮肥利用效率势必会降低并造成一定的环境问题。因此,根据马铃薯的需肥规律进行氮肥的施用,提高氮肥利用率已成为业界共识。然而,传统的氮肥推荐方法如土壤测试的方法费时费力、实时性差。随着遥感测试技术的发展,一些传感器应运而生,使基于光谱的植株氮营养快速检测成为可能。本文介绍了主动作物冠层传感器GreenSeeker对作物营养诊断的原理及其在玉米、小麦等作物上应用的国内外研究进展,并讨论其在马铃薯氮素营养诊断和精确施肥的重要性、可行性及应用前景。     

基于GreenSeeker的水稻氮素估测

为研究水稻植株氮素指标与GreenSeeker植被指数的定量关系,通过设置不同年份、不同氮肥水平的田间试验,于移栽后定期使用GreenSeeker获取冠层归一化差值植被指数(NDVI)和比值植被指数(RVI),并同步破坏性取样获取植株生物量和氮含量,分析不同品种和不同氮营养条件下氮素和植被指数(NDVI和RVI)变化规律,建立基于NDVI和RVI的氮素监测模型。结果表明,植株氮含量可以基于NDVI和RVI分时期进行估算,植株氮积累量可以被分阶段反演。利用GreenSeeker可以实现水稻氮素快速无损监测,为水稻氮肥精确管理提供技术支持。     

Nondestructive estimation of bok choy nitrogen status with an active canopy sensor in comparison to a chlorophyll meter

Precise estimation of vegetable nitrogen (N) status is critical in optimizing N fertilization management. However, nondestructive and accurate N diagnostic methods for vegetables are relatively scarce. In our two-year field experiment, we evaluated whether an active canopy sensor (GreenSeeker) could be used to nondestructively predict N status of bok choy (Brassica rapa subsp. chinensis) compared with a chlorophyll meter. Results showed that the normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) generated by the active canopy sensor were well correlated with the aboveground biomass (AGB) (r=0.698-0.967), plant N uptake (PNU) (r=0.642-0.951), and root to shoot ratio (RTS) (r=-0.426 to -0.845). Compared with the chlorophyll meter, the active canopy sensor displayed much higher accuracy (5.0%-177.4% higher) in predicting AGB and PNU and equal or slightly worse (0.54-1.82 times that of the chlorophyll meter) for RTS. The sensor-based NDVI model performed equally well in estimating AGB (R²=0.63) and PNU (R²=0.61), but the meter-based model predicted RTS better (R²=0.50). Inclusion of the days after transplanting (DAT) significantly improved the accuracy of sensor-based AGB (19.0%-56.7% higher) and PNU (24.6%-84.6% higher) estimation models. These findings suggest that the active canopy sensor has a great potential for nondestructively estimating N status of bok choy accurately and thus for better N recommendations, especially with inclusion of DAT, and could be applied to more vegetables with some verification.     

利用GreenSeeker法诊断春玉米氮素营养状况的研究

利用GreenSeeker对不同氮素处理春玉米冠层和叶片进行了测定,分析了植被指数(NDVI)和叶片叶绿素a(Chla)含量和氮素(N)含量的相关性。结果表明,不同氮素处理冠层NDVI变化与叶片叶绿素a含量在整个生育期内变化趋势一致。NDVI对叶绿素变化最敏感的时期是大喇叭口期。从不同叶位的变化趋势看,NDVI随CHla、N含量的变化而表现出明显的变化,三者之间具有显著的直线相关关系。利用GreenSeeker获取的NDVI值为生产中诊断春玉米叶绿素或氮素状况提供了有效手段。     

Can Oklahoma Mesonet Cumulative Evapotranspiration Data Be Accurately Predicted Using Three Interpolation Methods?

The Oklahoma Mesonet, an automated statewide system of 115 remote meteorological stations, provides observations through an interactive website, www.mesonet.org. Precision sensing enables estimation of winter wheat grain yield potential in midseason, which in turn has potential to increase fertilizer-use efficiency. Knowing cumulative evapotranspiration could help to improve the accuracy of yield potential predictions. We evaluated how well the evapotranspiration value of a chosen test station can be predicted from values of surrounding Oklahoma Mesonet stations using the nearest neighbor, local average, and the inverted weighted distance methods. All three interpolation methods enabled us to accurately predict the actual cumulative evapotranspiration value at the test Oklahoma Mesonet station. The nearest neighbor method is the easiest and the quickest interpolation method, and it also proved the most accurate (R² = 0.98). Results of this study underline the value of Oklahoma Mesonet weather data to Oklahoma crop producers for improved fertilizer-use efficiency.     

基于SPAD-502与GreenSeeker的冬小麦氮营养诊断研究

用GreenSeeker和SPAD-502测定了不同氮素处理的冬小麦冠层NDVI与叶片SPAD值, 分析了它们与叶片全氮、叶绿素含量及产量间的关系。结果表明: 冬小麦抽穗期SPAD值和NDVI值均与叶绿素含量呈极显著正相关; 除抽穗期和返青期外, SPAD值与叶氮含量、叶绿素含量的相关系数在其余各生育期均达到显著或极显著水平; NDVI值与叶氮含量、叶绿素含量在拔节期、乳熟期的相关性同SPAD值一致; SPAD值可以进行叶绿素的诊断, NDVI值可以进行氮的诊断。氮营养诊断时期应该选择拔节期。通过回归建立了基于SPAD值、NDVI值的产量估测模型, 可以通过SPAD值、NDVI值对冬小麦产量进行估算。