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1.
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 (YP0) 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 YP0 and RI. The objectives of this study were twofold: (1) identify important vegetation indices obtained from Crop Circle ACS-470 sensor for estimating rice YP0 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 R2 = 0.66 and 0.70, respectively) and Crop Circle (best R2 = 0.71 and 0.77, respectively) sensors worked well for estimating YP0 and RI at the stem elongation stage. At the booting stage, Crop Circle red edge optimized soil adjusted vegetation index (REOSAVI, R2 = 0.82) and green ratio vegetation index (R2 = 0.73) explained 26 and 22 % more variability in YP0 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 YP0 or RI estimation, while Crop Circle REOSAVI and normalized green index could still explain more than 70 % of YP0 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 YP0 at later growth stages.  相似文献   

2.
Researchers from Colorado State University, in collaboration with scientists from the United States Department of Agriculture (USDA), initiated a long-term multi-disciplinary study in precision agriculture in 1997. Site-specific management zones (SSMZ) were investigated as a means of improving nitrogen management in irrigated maize cropping systems. The objective was to develop precise nutrient management strategies for semi-arid irrigated cropping systems. This study was conducted in five fields in northeastern Colorado, USA. Two techniques for delineating management zones were developed and compared: SSMZ and yield-based management zones (YBMZ). Nitrogen uptake and grain yield differences among SSMZs were compared as were soil properties. Both management zone techniques were used to divide fields into smaller units that were different with regard to productivity potential (e.g., high zones had high productivity potential while low zones had low productivity potential). Economic analysis was also performed. Based on grain yield productivity, the SSMZs performed better than the YBMZ technique in most cases. Grain yield and N uptake between the low and high productivity management zones were statistically different for most site-years and N fertilizer rates (p < 0.05). Soil properties helped to explain the productivity potential of the management zones. The low SSMZ was markedly different from the high SSMZ based on bulk density, organic carbon, sand, silt, porosity and soil moisture. Net returns ranged from 188 to 679 USD ha?1. In two out of three site-years the variable yield goal strategy resulted in the largest net returns. In this study, the SSMZ approach delineates areas of different productivity accurately across the agricultural fields. The SSMZs are different with regard to soil properties as well as grain yield and N uptake. Site-specific management zones are an inexpensive and pragmatic approach to precise N management in irrigated maize.  相似文献   

3.
Proximal sensing, or obtaining information from close range, is a potentially useful tool for measuring the crop nitrogen status in real-time The objective of this study was to use proximal sensing of crop canopy spectral reflectance to evaluate variable-rate application of nitrogen in terms of its effect on yield and grain quality of winter wheat (Triticum aestivum L.). The sensor used was the Hydro-Precise N-Sensor System. Yield and grain quality maps were used as a basis for full-scale field trials with winter wheat growing under four nitrogen application treatments: a large (274 kg ha?1), recommended (167 kg ha?1) and two sensor-assisted (167 kg ha?1) rates. The recommended rate of 167 kg N ha?1 was given in a three-split application that meets the present Danish regulations to reduce nitrogen leaching. These require arable farmers to decrease nitrogen fertilizer application to 90% of the economically optimal level. Each farm’s baseline is calculated to take into account land quality, land allocated to each crop, and crop rotation. In the two sensor-assisted applications the Hydro-Precise N-Sensor System directs the last two of the three-split N application. Grain samples were collected directly from the grain flow of a combine harvester and analysed for protein, water and starch content. Grain data were related to and compared with combine yield meter registrations. Within the field, the variances of protein yield (698–1208 kg ha?1) and grain protein (9.5–13.4%) were large. The nitrogen application treatments affected the average protein content (10.5–12.3%) and grain yield (9.87–10.42 t ha?1) strongly. The grain starch content was largest in the uniform and sensor applied systems and smallest in the high nitrogen application treatment. Applying nitrogen according to the Hydro-Precise N-Sensor System did not increase grain yield or the protein and starch contents. Minor differences only were observed in both protein content and yield between uniform-rate N application and sensor-based variable-rate N application.  相似文献   

4.
Wheat aphid, Sitobion avenae F. is one of the most destructive insects infesting winter wheat and appears almost annually in northwest China. Past studies have demonstrated the potential of remote sensing for detecting crop diseases and insect damage. This study aimed to investigate the spectroscopic estimation of leaf aphid density by applying continuous wavelet analysis to the reflectance spectra (350–2 500 nm) of 60 winter wheat leaf samples. Continuous wavelet transform (CWT) was performed on each of the reflectance spectra to generate a wavelet power scalogram compiled as a function of wavelength location and scale of decomposition. Linear regression between the wavelet power and aphid density was to identify wavelet features (coefficients) that might be the most sensitive to aphid density. The results identified five wavelet features between 350 and 2 500 nm that provided strong correlations with leaf aphid density. Spectral indices commonly used to monitor crop stresses were also employed to estimate aphid density. Multivariate linear regression models based on six sensitivity spectral indices or five wavelet features were established to estimate aphid density. The results showed that the model with five wavelet features (R2 = 0.72, RMSE = 16.87) performed better than the model with six sensitivity spectral indices (R2 = 0.56, RMSE = 21.19), suggesting that the spectral features extracted through CWT might potentially reflect aphid density. The results also provided a new method for estimating aphid density using remote sensing.  相似文献   

5.
Advances in precision agriculture technology have led to the development of ground-based active remote sensors that can determine normalized difference vegetation index (NDVI). Studies have shown that NDVI is highly related to leaf nitrogen (N) content in maize (Zea mays L.). Remotely sensed NDVI can provide valuable information regarding in-field N variability and significant relationships between sensor NDVI and maize grain yield have been reported. While numerous studies have been conducted using active sensors, none have focused on the comparative effectiveness of these sensors in maize under semi-arid irrigated field conditions. Therefore, the objectives of this study were (1) to determine the performance of two active remote sensors by determining each sensor’s NDVI relationship with maize N status and grain yield as driven by different N rates in a semi-arid irrigated environment and, (2) to determine if inclusion of ancillary soil or plant data (soil NO3 concentration, leaf N concentration, SPAD chlorophyll and plant height) would affect these relationships. Results indicated that NDVI readings from both sensors had high r 2 values with applied N rate and grain yield at the V12 and V14 maize growth stages. However, no single or multiple regression using soil or plant variables substantially increased the r 2 over using NDVI alone. Overall, both sensors performed well in the determination of N variability in irrigated maize at the V12 and V14 growth stages and either sensor could be an important tool to aid precision N management.  相似文献   

6.
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 Nmin 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 (R2 = 0.78) and grain yield (R2 = 0.62) of winter wheat over GS sensor (R2 = 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.  相似文献   

7.
Site-specific crop management is a promising approach to maximize crop yield with optimal use of rapidly depleting natural resources. Availability of high resolution crop data at critical growth stages is a key for real-time data-driven decisions during the production season. The goal of this study was to evaluate the possibility of using small unmanned aerial system (UAS)-based remote sensing technologies to monitor the crop stress of irrigated pinto beans (Phaseolus vulgaris L.) with varied irrigation and tillage treatments. A small UAS with onboard multispectral and infrared thermal imaging sensors was used to collect data from bean field plots on three growth stages in 2015 and 2016, respectively. Indicators including green normalized vegetation index (GNDVI), canopy cover (CC, ratio of ground covered by crop canopy to the total plot area) and canopy temperature (CT, °C) of crops were extracted from imaging data and correlated with ground-reference crop yield and leaf area index (LAI) estimated with a handheld ceptometer. Results show that GNDVI, CC and CT were able to differentiate crops with full and deficit irrigation treatments at each of the three growth stages in both years. Developed indicators were strongly correlated with to the crop yield with Pearson correlation coefficients (r) of approximate 0.71 and 0.72 for GNDVI and CC, respectively, in the early growth stage (54 days after planting) in both years. Canopy temperature showed even stronger correlation (r > 0.8) with yield at early growth stage. Performance of small UAS-based imagery-based indicators in crop stress monitoring and crop yield estimation was better than or comparable to that of the ground-based LAI estimates, indicating the potential of such remote sensing tool in rapid crop stress monitoring and management.  相似文献   

8.
When utilizing optical sensors to make in-season agronomic recommendations in winter wheat, one parameter often required is the in-season grain yield potential at the time of sensing. Current estimates use an estimate of biomass, such as normalized difference vegetation index (NDVI), and growing degree days (GDDs) from planting to NDVI data collection. The objective of this study was to incorporate soil moisture data to improve the ability to predict final grain yield in-season. Crop NDVI, GDDs that were adjusted based upon if there was adequate water for crop growth, and the amount of soil profile (0–0.80 m) water were incorporated into a multiple linear regression model to predict final grain yield. Twenty-two site-years of N fertility trials with in-season grain yield predictions for growth stages ranging from Feekes 3 to 10 were utilized to calibrate the model. Three models were developed: one for all soil types, one for loamy soil textured sites, and one for coarse soil textured sites. The models were validated with 11 independent site-years of NDVI and weather data. The results indicated there was no added benefit to having separate models based upon soil types. Typically, the models that included soil moisture, more accurately predicted final grain yield. Across all site years and growth stages, yield prediction estimates that included soil moisture had an R2 = 0.49, while the current model without a soil moisture adjustment had an R2 = 0.40.  相似文献   

9.
Nitrogen (N) fertilizer application can lead to increased crop yields but its use efficiency remains generally low which can cause environmental problems related to nitrate leaching as well as nitrous oxide emissions to the atmosphere. The objectives of this study were to: (i) to demonstrate that properly identified variable rates of N fertilizer lead to higher use efficiency and (ii) to evaluate the capability of high spectral resolution satellite to detect within-field crop N response using vegetation indices. This study evaluated three N fertilizer rates (30, 70, and 90 kg N ha?1) and their response on durum wheat yield across the field. Fertilizer rates were identified through the adoption of the SALUS crop model, in addition to a spatial and temporal analysis of observed wheat grain yield maps. Hand-held and high spectral resolution satellite remote sensing data were collected before and after a spring side dress fertilizer application with FieldSpec, HandHeld Pro® and RapidEye?, respectively. Twenty-four vegetation indices were compared to evaluate yield performance. Stable zones within the field were defined by analyzing the spatial stability of crop yield of the previous 5 years (Basso et al. in Eur J Agron 51: 5, 2013). The canopy chlorophyll content index (CCCI) discriminated crop N response with an overall accuracy of 71 %, which allowed assessment of the efficiency of the second N application in a spatial context across each management zone. The CCCI derived from remotely sensed images acquired before and after N fertilization proved useful in understanding the spatial response of crops to N fertilization. Spectral data collected with a handheld radiometer on 100 grid points were used to validate spectral data from remote sensing images in the same locations and to verify the efficacy of the correction algorithms of the raw data. This procedure was presented to demonstrate the accuracy of the satellite data when compared to the handheld data. Variable rate N increased nitrogen use efficiency with differences that can have significant implication to the N2O emissions, nitrate leaching, and farmer’s profit.  相似文献   

10.
基于开花期卫星遥感数据的大田小麦估产方法比较   总被引：4，自引：2，他引：2  
谭昌伟  杜颖  童璐  周健  罗明  颜伟伟  陈菲 《中国农业科学》2017,50(16):3101-3109
【目的】卫星遥感具有覆盖范围广、获取速度快、信息量大、动态性强等优势,能够及时准确地获取作物产量信息,反映作物产量空间变化趋势。遥感技术作物估产已成为现代农业生产中研究热点。通过改善遥感估产建模方法,以实现进一步提高大田作物遥感估产精度,为宏观了解不同区域作物产量形成情况及变化趋势提供直观、可靠的参考。【方法】论文结合2011—2012年江苏省大丰、兴化、姜堰、泰兴、仪征5个县区的定点观测试验,以国产卫星产品HJ-1A/1B影像为遥感数据,于小麦开花期开展大田定位观测区卫星遥感植被指数、关键生长指标与收获期单产间的定量分析。通过对产量与小麦生长指标以及植被指数进行定量关系分析,进一步增强遥感反演的机理性和重演性。将卫星遥感变量与小麦产量进行相关关系分析作为遥感估产的直接建模方法,间接建模方法则是选取与产量相关性较好的遥感变量以及与遥感变量相关性较好的主要苗情指标,利用筛选得到的敏感遥感变量,首先监测对应的小麦生长指标,结合该小麦生长指标与产量间的定量关系,进而建立间接估产模型,利用此模型进行小麦遥感间接估产。利用直接和间接建模方法,以相关性最高为原则,筛选估算产量的敏感卫星遥感变量。以2012年试验数据为建模样本,采用线性回归分析方法,分析小麦开花期苗情指标、产量与卫星遥感变量两两之间的相关性,分别构建以遥感植被指数为基础的大田小麦估产模型,与地面实测结果一起建立模型共同分析。以2011年试验数据为验证样本,选取评价指标拟合度(R2)和均方根误差(RMSE),对两类模型的估算精度进行验证和比较,以提高遥感反演的定量化水平和可信度。【结果】分别以差值植被指数(difference vegetation index,DVI)和比值植被指数(ratio vegetation index,RVI)为基础的单因子直接估产模型的均方根误差(root mean square error,RMSE)为918 kg·hm-2和1 399.5 kg·hm-2,以DVI和RVI遥感变量构建双变量估产模型的RMSE为1 036.5 kg·hm-2,以归一化植被指数(normalized difference vegetation index,NDVI)和叶片氮积累量为基础构建的间接估产模型的RMSE为805.5 kg·hm-2,说明开花期HJ-1A/1B影像估算小麦区域产量是可行的,且精度较高;经比较,以NDVI和叶片氮积累量为基础的间接估产模型精度明显高于直接估产模型,相较于DVI直接估产模型RMSE降低了112.5 kg·hm-2,相较于RVI直接估产模型RMSE降低了594 kg·hm-2,相较于双因子模型RMSE降低了231 kg·hm-2。【结论】国产卫星HJ-1A/B可以较好满足估测小麦产量要求,且利用间接方法建立作物遥感估产模型要好于直接方法,研究结果为利用遥感技术更为准确估算大田小麦产量提供了一种新的途径。  相似文献   

11.
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12.
This study proposes a new method for inverting radiative transfer models to retrieve canopy biophysical parameters using remote sensing imagery. The inversion procedure is improved with respect to standard inversion, and achieves simultaneous inversion of leaf area index (LAI), soil reflectance (ρsoil), chlorophyll content (Ca+b) and average leaf angle (ALA). In this approach, LAI is used to constrain modelling conditions during the inversion process, providing information about the phenological state of each plot under study. Due to the small area of the vegetation plots used for the inversion procedure and in order to avoid redundant information and improve computation efficiency, existing plot segmentation was used. All retrieved biophysical parameters, except LAI, were assumed to be invariant within each plot. The proposed methodology, based on the combination of PROSPECT and SAILH models, was tested over 16 cereal fields and 51 plots, on two dates, which were chosen to ensure crop assessment at different phenological stages. Plots were selected to provide a wide range of LAI between 0 and 6. Field measurements of LAI, ALA and Ca+b were conducted and used as ground truth for validation of the proposed model-inversion methodology. The approach was applied to very high spatial resolution remote sensing data from the QuickBird 2 satellite. The inversion procedure was successfully applied to the imagery and retrieved LAI with R 2 = 0.83 and RMSE = 0.63 when compared to LAI2000 ground measurements. Separate inversions for barley and wheat yielded R 2 = 0.89 (RMSE = 0.64) and R 2 = 0.56 (RMSE = 0.61), respectively.  相似文献   

13.
基于PyWOFOST作物模型的东北玉米估产及精度评估   总被引：2，自引：0，他引：2  
【目的】构建合理的作物估产方案,提高作物估产精度。【方法】本文以基于集合卡尔曼滤波（Ensemble Kalman Filter,EnKF）构建的遥感信息-作物模型结合模型（PyWOFOST）为基础,建立了以LAI为结合点,适用于中国东北地区玉米的同化模拟模型,并使用MODIS LAI数据作为外部同化数据进行同化模拟,重点分析了遥感观测（MODIS LAI）和模型参数（出苗-开花期所需积温,TSUM1）的不确定性（即随机误差）对同化模拟结果的影响。最后,利用PyWOFOST模型实现了区域尺度上的玉米估产。【结果】同化外部观测数据后的玉米模拟产量较未同化外部数据的模拟产量有明显改善,20个未受灾害影响的农气站玉米产量同化前的模拟误差及在TSUM1的不确定性为0、10、20、30℃时的同化后模拟误差分别为14.04%、12.71%、11.91%、10.44%及10.48%;同化后的模拟LAI普遍较同化前的模拟LAI更接近实测LAI,更符合玉米LAI的变化趋势;同化前模拟发育期与实测发育期平均绝对误差为3.4 d,而同化后在TSUM1的不确定性为0、10、20、30℃时模拟发育期与实测发育期的平均误差分别为3.5、4.3、5.0、5.5 d。区域尺度上玉米估产结果表明,58.82%的区域玉米估产误差在15%以内,同化产量和统计产量的确定系数为0.806。【结论】基于集合卡尔曼滤波同化遥感信息进行作物估产是可行的。  相似文献   

14.
用PLS算法由HJ-1A/1B遥感影像估测区域冬小麦理论产量   总被引：1，自引：0，他引：1  
谭昌伟  罗明  杨昕  马昌  严翔  周健  杜颖  王雅楠 《中国农业科学》2015,48(20):4033-4041
【目的】作物遥感估产是遥感技术在农业生产中研究与应用的重点领域,能够向大田区域生产提供及时可靠的产量信息,准确地估测作物产量,对于确保国家粮食安全,制定社会发展规划,指导和调控宏观种植业结构调整,提高涉农企业与农民的经营管理水平具有重要意义,为进一步提高遥感估产精度,显示国产影像在农业估产中的应用效果。通过筛选冬小麦理论产量的敏感遥感变量,构建基于国产影像的理论产量遥感估测模型,实现区域冬小麦理论产量遥感估测,为及时了解不同生态区域冬小麦产量丰欠变化趋势提供参考。【方法】以2010年4月26日、2011年4月28日、2012年4月28日和2013年5月2日冬小麦开花期四景HJ-1A/1B影像为遥感数据,提取出13个遥感变量,以江苏省泰兴、姜堰、仪征、兴化、大丰5县作为试验采样区,于各实验区选取具有代表性的样点进行采样,并于室内进行测定,将335个实测的冬小麦理论产量样本按3﹕2比例分成建模集和验证集样本,依据估算残差平方和处于最小值确定模型所需主成分数,将决定系数、均方根误差和相对误差为模型评价参数,利用建模集样本分析了卫星遥感变量与冬小麦理论产量的定量关系,运用偏最小二乘回归算法构建及验证了以理论单产为目标的多变量遥感估产模型,将其算法模型估产效果与线性回归算法和主成分分析算法模型进行比较,并制作了冬小麦理论产量空间等级分布图。【结果】理论产量与所选的大多数遥感变量间关系密切,且多数遥感变量两两间具有极显著的多重相关性;理论产量偏最小二乘回归模型的最佳主成分数为4,且结构加强色素植被指数、归一化植被指数、绿色归一化植被指数和植被衰减指数为理论产量遥感估测的敏感变量;经建模集和验证集评价,理论产量估测模型的决定系数分别为0.79和0.76,均方根误差分别为720.45和928.05 kg·hm-2,相对误差分别为11.45%和13.92%,且估测精度比线性回归算法分别提高了25%以上和27%以上,比主成分分析算法分别提高了15%以上和16%以上,说明偏最小二乘回归算法模型估测区域理论产量的效果明显好于线性回归和主成分分析算法,且具有较强的应用能力。【结论】该模型应用结果与冬小麦理论产量实际区域分布情况相符合,为提高遥感对区域冬小麦理论产量的估测精度提供了一种有效途径,有利于大面积应用和推广。  相似文献   

15.
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16.
Spatial dynamics of crop yield provide useful information for improving the production. High sensitivity of crop growth models to uncertainties in input factors and parameters and relatively coarse parameterizations in conventional remote sensing(RS) approaches limited their applications over broad regions. In this study, a process-based and remote sensing driven crop yield model for maize(PRYM–Maize) was developed to estimate regional maize yield, and it was implemented using eight data-model coupling strategies(DMCSs) over the Northeast China Plain(NECP). Simulations under eight DMCSs were validated against the prefecture-level statistics(2010–2012) reported by National Bureau of Statistics of China, and inter-compared. The 3-year averaged result could give more robust estimate than the yearly simulation for maize yield over space. A 3-year averaged validation showed that prefecture-level estimates by PRYM–Maize under DMCS8, which coupled with the development stage(DVS)-based grain-filling algorithm and RS phenology information and leaf area index(LAI), had higher correlation(R, 0.61) and smaller root mean standard error(RMSE, 1.33 t ha~(–1)) with the statistics than did PRYM–Maize under other DMCSs. The result also demonstrated that DVS-based grain-filling algorithm worked better for maize yield than did the harvest index(HI)-based method, and both RS phenology information and LAI worked for improving regional maize yield estimate. These results demonstrate that the developed PRYM–Maize under DMCS8 gives reasonable estimates for maize yield and provides scientific basis facilitating the understanding the spatial variations of maize yield over the NECP.  相似文献   

17.
Previous studies have shown the importance of soil moisture (SM) in estimating crop yield potential (YP). The sensor based nitrogen (N) rate calculator (SBNRC) developed by Oklahoma State University utilizes the Normalized Difference Vegetation Index (NDVI) and the in-season estimated yield (INSEY) as the estimate of biomass to assess YP and to generate N recommendations based on estimated crop need. The objective was to investigate whether including the SM parameter into SBNRC could help to increase the accuracy of YP prediction and improve N rate recommendations. Two experimental sites (Lahoma and Perkins) in Oklahoma were established in 2006/07 and 2007/08. Wheat spectral reflectance was measured using a GreenSeeker? 505 hand-held optical sensor (N-Tech Industries, Ukiah, CA). Soil–water content measured with matric potential 229-L sensors (Campbell Scientific, Logan, UT) was used to determine volumetric water content and fractional water index. The relationships between NDVI, INSEY and SM indices at planting and sensing at 5, 25, 60 and 75-cm depths versus grain yield (GY) were evaluated. Wheat GY, NDVI at Feekes 5 and soil WC at planting and as sensed at three depths were also analyzed for eight consecutive growing seasons (1999–2006) for Lahoma. Incorporation of SM into NDVI and INSEY calculations resulted in equally good prediction of wheat GY for all site-years. This indicates that NDVI alone was able to account for the lack of SM information and thus lower crop YP. Soil moisture data, especially at the time of sensing at the 5-cm depth could assist in refining winter wheat YP prediction.  相似文献   

18.
The research reported here seeks to determine whether it is necessary to obtain optical reflectance measurements with a GreenSeeker® handheld sensor from each field to make accurate in-season nitrogen application recommendations for winter wheat, and how much precision—and profit—would be lost by moving from site-specific (or field-specific) optical reflectance sampling to region-level sampling. The approach used was to estimate a separate linear response-plateau regression every year using yield and optical reflectance data from randomized complete block experiments. Profits from region-level sampling and field-level sampling were statistically indistinguishable, but this result was mostly due to both being imprecise. Furthermore, the region- and field-based sampling systems were no better than break-even with the historical extension advice to apply preplant anhydrous ammonia at 90 kg ha?1. The approach of estimating a new regression every year is too imprecise, whether at the field or region level. This research goes beyond past research by accounting for the uncertainty in the estimated relationships. The poor performance of the systems is directly related to the imprecise relationship between yield and optical reflectance responses to nitrogen.  相似文献   

19.
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−1, with an average of 142.6 kg ha−1 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−1 across the seven fields, with an average of 173 kg ha−1 and a CV of 46%. Field-specific N management could save an average of 128 kg N ha−1 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.  相似文献   

20.
Optical sensors, coupled with mathematical algorithms, have proven effective at determining more accurate mid-season nitrogen (N) fertilizer recommendations in winter wheat. One parameter required in making these recommendations is in-season grain yield potential at the time of sensing. Four algorithms, with different methods for determining grain yield potential, were evaluated for effectiveness to predict final grain yield and the agronomic optimum N rate (AONR) at 34 site-years. The current N fertilizer optimization algorithm (CNFOA) outperformed the other three algorithms at predicting yield potential with no added N and yield potential with added N (R2 = 0.46 and 0.25, respectively). However, no differences were observed in the amount of variability accounted for among all four algorithms in regards to predicting the AONR. Differences were observed in that the CNFOA and proposed N fertilizer optimization algorithm (PNFOA), under predicted the AONR at approximately 75 % of the site-years; whereas, the generalized algorithm (GA) and modified generalized algorithm (MGA) recommended N rates under the AONR at about 50 % of the site-years. The PNFOA was able to determine N rate recommendations within 20 kg N ha?1 of the AONR for half of the site-years; whereas, the other three algorithms were only able recommend within 20 kg N ha?1 of the AONR for about 40 % of the site-years. Lastly, all four algorithms reported more accurate N rate recommendations compared to non-sensor based methodologies and can more precisely account for the year to year variability in grain yields due to environment.  相似文献   

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