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J A Stunkard F T Szatalowicz H C Sudduth 《American journal of veterinary research》1971,32(11):1873-1878
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Wang Dechun Prato Tony Qiu Zeyuan Kitchen Newell R. Sudduth Kenneth A. 《Precision Agriculture》2003,4(1):35-52
Variable Rate Technology (VRT) has the potential to increase crop yields and improve water quality relative to Uniform Rate Technology (URT). The effects on profitability and water quality of adopting VRT for nitrogen (N) and lime were evaluated for corn production on four claypan soil fields in north central Missouri under average to better than average weather conditions. Variable N and lime rates were based on measured topsoil depth and soil pH, respectively. VRT rates were compared to two different uniform N applications (URT-Nl based on the topsoil depth within these claypan soil fields, and URT-N2 based on a typical N rate for corn production in this area). Expected corn yield was predicted based on topsoil depth, soil pH, N rate, and lime rate. Water quality benefits of VRT relative to URT were evaluated based on potential leachable N. Sensitivity analyses were performed using simulated topsoil data for topsoil depth and soil pH. Results showed that VRT was more profitable than URT in the four sample fields under URT-N1, and in two of the four fields under URT-N2. Greater variation in topsoil depth and soil pH resulted in higher profitability and greater water quality benefits with VRT. Results support adoption of VRT for N and lime application for other claypan soil fields with characteristics similar to those in the fields used in this study. 相似文献
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Pingping Jiang Zhuoqiong He Newell R. Kitchen Kenneth A. Sudduth 《Precision Agriculture》2009,10(2):111-127
Understanding relationships of soil and field topography to crop yield within a field is critical in site-specific management
systems. Challenges for efficiently assessing these relationships include spatially correlated yield data and interrelated
soil and topographic properties. The objective of this analysis was to apply a spatial Bayesian hierarchical model to examine
the effects of soil, topographic and climate variables on corn yield. The model included a mean structure of spatial and temporal
co-variates and an explicit random spatial effect. The spatial co-variates included elevation, slope and apparent soil electrical
conductivity, temporal co-variates included mean maximum daily temperature, mean daily temperature range and cumulative precipitation
in July and August. A conditional auto-regressive (CAR) model was used to model the spatial association in yield. Mapped corn
yield data from 1997, 1999, 2001 and 2003 for a 36-ha Missouri claypan soil field were used in the analysis. The model building
and computation were performed using a free Bayesian modeling software package, WinBUGS. The relationships of co-variates
to corn yield generally agreed with the literature. The CAR model successfully captured the spatial association in yield.
Model standard deviation decreased about 50% with spatial effect accounted for. Further, the approach was able to assess the
effects of temporal climate co-variates on corn yield with a small number of site-years. The spatial Bayesian model appeared
to be a useful tool to gain insights into yield spatial and temporal variability related to soil, topography and growing season
weather conditions.
相似文献
| Pingping JiangEmail: |
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Feng Aijing Zhou Jianfeng Vories Earl D. Sudduth Kenneth A. 《Precision Agriculture》2022,23(4):1248-1275
Precision Agriculture - Quantification of interactions of soil conditions, plant available water and weather conditions on crop development and production is the key for optimizing field management... 相似文献
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利用可见-近红外光谱(VNIR)检测土壤磷(P)和钾(K)含量存在精度不高的问题。为消除土壤质地、类型、颜色、颗粒大小、形状、密度等对光谱精度的影响,利用VNIR检测土壤P和K,采用直接正交信号校正(DOSC)光谱预处理降低干扰的方法,对美国密苏里州8种类型土壤共1 582个土壤样品、在350~2 500 nm波段内进行VNIR光谱扫描,对光谱进行吸光度、均值归一化、5点均值滤波平滑处理后,分别用DOSC处理和未处理数据建立偏最小二乘回归(PLSR)分析模型对P和K进行预测。实验结果表明,DOSC处理后得到的模型预测精度显著提高,其预测均方根误差(RMSEP)降低21.50%(P)、26.93%(P(0,27))、24.64%(K)和27.67%(K(0,192)),预测决定系数(R2)提高85.76%(P)、108.31%(P(0,27))、59.38%(K)和87.01%(K(0,192)),相对分析误差(RPD)提高27.37%(P)、36.90%(P(0,27))、32.75%(K)和38.29%(K(0,192))。用DOSC算法在多类型土壤的P和K的VNIR测定中,能消除由于土壤质地、类型等引起的噪声信息,提高模型预测精度,为多类型土壤P和K的VNIR测定提供了一种光谱预处理方法。 相似文献
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M.?A.?YostEmail author N.?R.?Kitchen K.?A.?Sudduth E.?J.?Sadler S.?T.?Drummond M.?R.?Volkmann 《Precision Agriculture》2017,18(5):823-842
Research is lacking on the long-term impacts of field-scale precision agriculture practices on grain production. Following more than a decade (1993–2003) of yield and soil mapping and water quality assessment, a multi-faceted, ‘precision agriculture system’ (PAS) was implemented from 2004 to 2014 on a 36-ha field in central Missouri. The PAS targeted management practices that address crop production and environmental issues. It included no-till, cover crops, growing winter wheat (Triticum aestivum L.) instead of corn (Zea mays L.) for field areas where corn was not profitable, site-specific N for wheat and corn using canopy reflectance sensing, variable-rate P, K and lime using intensively grid-sampled data, and targeting of herbicides based on weed pressure. The PAS assessment was accomplished by comparing it to the previous decade of conventional, whole-field corn-soybean (Glycine max L.) mulch-tillage management. In the northern part of the field and compared to pre-PAS corn, relative grain yield of wheat in PAS was greatly improved and temporal yield variation was reduced on shallow topsoil, but relative grain yield was reduced on deep soil in the drainage channel. In the southern part of the field where corn remained in production, PAS did not lead to increased yield, but temporal yield variation was reduced. Across the whole field, soybean yield and temporal yield variation were only marginally influenced by PAS. Spatial yield variation of all three crops was not altered by PAS. Therefore, the greatest production advantage of a decade of precision agriculture was reduced temporal yield variation, which leads to greater yield stability and resilience to changing climate. 相似文献
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Yost M. A. Kitchen N. R. Sudduth K. A. Massey R. E. Sadler E. J. Drummond S. T. Volkmann M. R. 《Precision Agriculture》2019,20(6):1177-1198
Precision Agriculture - After two decades of availability of grain yield-mapping technology, long-term trends in field-scale profitability for precision agriculture (PA) systems and conservation... 相似文献
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The application of crop simulation models in precision agriculture research appears to require only the specification of some
input parameters and then running the model for each desired location in a field. Reports in the extensive literature on modeling
have described independent tests for different cultivars, soil types and weather, and these have been presumed to validate
the model performance in general. However, most of these tests have evaluated model performance for simulating mean yields
for multiple plots in yield trials or in other large-area studies. Precision agriculture requires models to simulate not only
the mean, but also the spatial variation in yield. No consensus has emerged about how to test model performance rigorously,
or what level of performance is sufficient. In addition, many measures of goodness of fit between the observed and simulated
data (i.e., model performance) depend on the range of variation in the observed data. If, for example, inter-annual and spatial
sources of variation are combined in a test, poor performance in one might be masked by good performance in the other. Our
objectives are to: (1) examine research aims that are common in precision agriculture, (2) discuss expectations of model performance,
and (3) compare several traditional and some alternative measures of model performance. These measures of performance are
explained with examples that illustrate their limitations and strengths. The risk of relying on a test that combines spatial
and temporal data was shown with data where the overall fit was good (r
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> 0.8), but the fit within any year was zero. Information gained using these methods can both guide and help to build confidence
in future modeling efforts in precision agriculture.
相似文献
| E. John SadlerEmail: |
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