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Soil nutrients vary greatly and information regarding their spatial-temporal variability is critical for achieving site-specific nutrient management. However, the problem is that the high cost of soil sampling and laboratory analysis limits the number of samples current research budgets can afford. Besides, large sampling intervals sometimes fail to capture the variability of typical soil properties. Consequently, determining a reasonable yet feasible sampling interval to characterize the spatial variation of soil properties is greatly needed to delineate soil nutrient management zones (MZs). For this purpose, this study used four sampling intervals, 25 m × 25 m, 50 m × 50 m, 75 m × 75 m, and 100 m × 100 m by resampling the raw data acquired from seven adjacent fields located in Quzhou County to estimate the optimal design of soil sampling. Five soil properties (pH, electrical conductivity, extractable phosphorus, extractable potassium, and soil organic matter) were selected to delineate MZs using principal component analysis and fuzzy c-means clustering algorithm. The results indicated that indigenous P and K varied significantly, with the coefficients of variation of 34.33% and 24.01 %, respectively. The findings suggested that the optimal soil sampling interval was 50 m to accurately delineate MZs in medium- and small-scale farmlands. Besides, findings also demonstrated that site-specific P and K management could save approximately 21 kg P ha−1 and 30 kg K ha−1 as compared to conventional farming practices while ensuring target yield. The results can help make precision P and K management decisions in medium- and small-scale farming systems. [ABSTRACT FROM AUTHOR]

Active canopy sensor (ACS)-based precision nitrogen (N) management (PNM) is a promising strategy to improve crop N use efficiency (NUE). The GreenSeeker (GS) sensor with two fixed bands has been applied to improve winter wheat ( Triticum aestivum L.) N management in North China Plain (NCP). The Crop Circle (CC) ACS-470 active sensor is user configurable with three wavebands. The objective of this study was to develop a CC ACS-470 sensor-based PNM strategy for winter wheat in NCP and compare it with GS sensor-based N management strategy, soil N test-based in-season N management strategy and conventional farmer's practice. Four site-years of field N rate experiments were conducted from 2009 to 2013 to identify optimum CC vegetation indices for estimating early season winter wheat plant N uptake (PNU) and grain yield in Quzhou Experiment Station of China Agricultural University located in Hebei province of NCP. Another nine on-farm experiments were conducted at three different villages in Quzhou County in 2012/2013 to evaluate the performance of the developed N management strategy. The results indicated that the CC ACS-470 sensor could significantly improve estimation of early season PNU (R = 0.78) and grain yield (R = 0.62) of winter wheat over GS sensor (R = 0.60 and 0.33, respectively). All three in-season N management strategies achieved similar grain yield as compared with farmer's practice. The three PNM strategies all significantly reduced N application rates and increased N partial factor productivity (PFP) by an average of 61-67 %. It is concluded that the CC sensor can improve estimation of early season winter wheat PNU and grain yield as compared to the GS sensor, but the PNM strategies based on these two sensors perform equally well for improving winter wheat NUE in NCP. More studies are needed to further develop and evaluate these active sensor-based PNM strategies under more diverse on-farm conditions. [ABSTRACT FROM AUTHOR]

In-season site-specific nitrogen (N) management is a promising strategy to improve crop N use efficiency and reduce risks of environmental contamination. To successfully implement such precision management strategies, it is important to accurately estimate yield potential without additional topdressing N application (YP) as well as precisely assess the responsiveness to additional N application (RI) during the growing season. Previous research has mainly used normalized difference vegetation index (NDVI) or ratio vegetation index (RVI) obtained from GreenSeeker active crop canopy sensor with two fixed bands in red and near-infrared (NIR) spectrums to estimate these two parameters. The development of three-band Crop Circle active sensor provides a potential to improve in-season estimation of YP and RI. The objectives of this study were twofold: (1) identify important vegetation indices obtained from Crop Circle ACS-470 sensor for estimating rice YP and RI; and (2) evaluate their potential improvements over GreenSeeker NDVI and RVI. Four site-years of field N rate experiments were conducted in 2012 and 2013 at the Jiansanjiang Experiment Station of China Agricultural University located in Northeast China. The GreenSeeker and Crop Circle ACS-470 active canopy sensor with green, red edge, and NIR bands were used to collect rice canopy reflectance data at different key growth stages. The results indicated that both the GreenSeeker (best R = 0.66 and 0.70, respectively) and Crop Circle (best R = 0.71 and 0.77, respectively) sensors worked well for estimating YP and RI at the stem elongation stage. At the booting stage, Crop Circle red edge optimized soil adjusted vegetation index (REOSAVI, R = 0.82) and green ratio vegetation index (R = 0.73) explained 26 and 22 % more variability in YP and RI, respectively, than GreenSeeker NDVI or RVI. At the heading stage, the GreenSeeker sensor indices became saturated and consequently could not be used for YP or RI estimation, while Crop Circle REOSAVI and normalized green index could still explain more than 70 % of YP and RI variability. It is concluded that both sensors performed similarly at the stem elongation stage, but significantly better results were obtained by the Crop Circle sensor at the booting and heading stages. Furthermore, the results revealed that Crop Circle green band-based vegetation indices performed well for RI estimation while the red edge-based vegetation indices were the best for estimating YP at later growth stages. [ABSTRACT FROM AUTHOR]

Understanding spatial variability of indigenous nitrogen (N) supply (INS) is important to the implementation of precision N management (PNM) strategies in small scale agricultural fields of the North China Plain (NCP). This study was conducted to determine: (1) field-to-field and within-field variability in INS; (2) the potential savings in N fertilizers using PNM technologies; and (3) winter wheat ( Triticum aestivum L.) N status variability at the Feekes 6 stage and the potential of using a chlorophyll meter (CM) and a GreenSeeker active crop canopy sensor for estimating in-season N requirements. Seven farmer's fields in Quzhou County of Hebei Province were selected for this study, but no fertilizers were applied to these fields. The results indicated that INS varied significantly both within individual fields and across different fields, ranging from 33.4 to 268.4 kg ha, with an average of 142.6 kg ha and a CV of 34%. The spatial dependence of INS, however, was not strong. Site-specific optimum N rates varied from 0 to 355 kg ha across the seven fields, with an average of 173 kg ha and a CV of 46%. Field-specific N management could save an average of 128 kg N ha compared to typical farmer practices. Both CM and GreenSeeker sensor readings were significantly related to crop N status and demand across different farmer's fields, showing a good potential for in-season site-specific N management in small scale farming systems. More studies are needed to further evaluate these sensing technology-based PNM strategies in additional farmer fields in the NCP. [ABSTRACT FROM AUTHOR]