Delineation of management zones to improve nitrogen management of wheat

Peralta, Nahuel Raúl; Costa, José Luís; Balzarini, Mónica; Castro-Franco, Mauricio; Córdoba, Mariano; Bullock, D. G.

doi:10.1016/j.compag.2014.10.017

Cited by 115 publications

(82 citation statements)

References 44 publications

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“…It is a sounder approach not to use crop yield in homogeneous area delineation, as yield is a grower's ultimate goal and cannot be predicted months before harvesting. This choice is supported by other sources in the literature (Peralta et al, 2015;Farid et al, 2016).…”

Section: Discussionmentioning

confidence: 74%

“…2.5-fold increase in DBY and GY between minimum and maximum (Table 4), and a 100% GY increase between L and H area with clustering based on proximal and remote vegetation indices (Table 6). These are among the highest variations observed in wheat studies (Song et al, 2009;Peralta et al, 2015;Quebrajo et al, 2015;Basso et al, 2016).…”

Section: Discussionmentioning

confidence: 77%

“…PCA represents a common approach for reducing the number of variables, which is also echoed in studies on wheat spatial variation (Casa and Castrignanò, 2008;Peralta et al, 2015). In our work, submitting spectral vegetation indices to PCA was aimed at delineating homogeneous areas based on data covering a more extended wheat stage, allowing key practices (e.g., nitrogen top dress fertilisation) to be more easily carried out in a frame of SSM.…”

Section: Discussionmentioning

confidence: 99%

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Assessing wheat spatial variation based on proximal and remote spectral vegetation indices and soil properties

et al. 2017

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Assessing the spatial variation of soil and crop properties is the basis for site specific management of crop practices in precision agriculture applications. To this aim, proximal and remote spectral vegetation indices are increasingly replacing soil analysis. In this study the spatial variation of soil properties, proximal and remote spectral vegetation indices were compared in a winter wheat (Triticum aestivum L.) crop grown in a 4.15 ha field in northern Italy. Soil analysis (particle size distribution, pH, carbonates, C, total N, available P, exchangeable cations and electrical conductivity) was geo-referentially carried out; the proximal indices chlorophyll content by N-Tester and normalised difference vegetation index through GreenSeeker were determined in three dates during stem elongation; the remote indices PurePixel TM chlorophyll index and PurePixel TM vegetation index were determined through the Landsat 8 satellite in three dates during the same wheat stage. Dry biomass yield (DBY), grain yield (GY) and yield components were determined at harvest. Soil, proximal and remote data were submitted to principal component analysis (PCA), and the retained PCs were clustered to delineate areas at low, intermediate and high yield potential, based on soil parameters (CLUsp), proximal (CLUpi), and remote vegetation indices (CLUri). DBY and GY were significantly correlated with several soil parameters and vegetation indices. Spatial distribution of soil and crop data consistently depicted a low performing area (GY<3 Mg ha -1 ) and a high performing one (GY>8 Mg ha -1 ). CLUsp determined a lower GY difference between low and high performing area (+60%), compared to CLUpi and CLUri (almost +100%). In CLUsp and CLUpi the low and high performing area were of similar size (25 and 29% for the two respective areas in CLUsp; 25 and 33% in CLUpi), whereas in CLUri they were quite different (16 and 46%). Lastly, yield potential levels determined by vegetation indices (CLUpi and CLUri) exhibited a better degree of agreement with DBY and GY levels, than soil parameters (CLUsp). In exchange for this, the above referred soil parameters are quite consistent in time, allowing soil data to be used for more years. On concluding, PCA followed by clustering resulted in a robust delineation of field areas at different yield potential. This is the premise for developing research driven strategies of practical use.

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Section: Discussionmentioning

confidence: 74%

Section: Discussionmentioning

confidence: 77%

Section: Discussionmentioning

confidence: 99%

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Assessing wheat spatial variation based on proximal and remote spectral vegetation indices and soil properties

et al. 2017

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“…Therefore, it is desirable to find other more rapid and low cost means of obtaining information for detailed soil mapping (King et al, 2005). Measurements of apparent soil electrical conductivity (ECa) can be intensively recorded in an easy and inexpensive way, being one of the most reliable techniques to characterize within-field variability of edaphic properties (Moral et al, 2010;Peralta et al, 2015).…”

Section: Data Collection Using the Veris 3100mentioning

confidence: 99%

Use of geophysical survey as a predictor of the edaphic properties variability in soils used for livestock production

Peralta

Cicore

Marino

et al. 2015

Span. j. agric. res.

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The spatial variability in soils used for livestock production (i.e. Natraquoll and Natraqualf) at farm and paddock scale is usually very high. Understanding this spatial variation within a field is the first step for site-specific crop management. For this reason, we evaluated whether apparent electrical conductivity (ECa), a widely used proximal soil sensing technology, is a potential estimator of the edaphic variability in these types of soils. ECa and elevation data were collected in a paddock of 16 ha. Elevation was negatively associated with ECa. Geo-referenced soil samples were collected and analyzed for soil organic matter (OM) content, pH, the saturation extract electrical conductivity (EC ext ), available phosphorous (P), and anaerobically incubated Nitrogen (Nan). Relationships between soil properties and ECa were analyzed using regression analysis, principal components analysis (PCA), and stepwise regression. Principal components (PC) and the PC-stepwise were used to determine which soil properties have an important influence on ECa. In this experiment elevation was negatively associated with ECa. The data showed that pH, OM, and EC ext exhibited a high correlation with ECa (R 2 =0.76; 0.70 and 0.65, respectively). Whereas P and Nan showed a lower correlation (R 2 =0.54 and 0.11 respectively). The model resulting from the PC-stepwise regression analysis explained slightly more than 69% of the total variation of the measured ECa, only retaining PC1. Therefore, EC ext , pH and OM were considered key latent variables because they substantially influence the relationship between the PC1 and the ECa (loading factors>0.4). Results showed that ECa is associated with the spatial distribution of some important soil properties. Thus, ECa can be used as a support tool to implement site-specific management in soils for livestock use.Additional key words: multivariate techniques; soil properties; geographic information system; lowland soils; spatial variability. Abbreviations used: ECa (apparent soil electrical conductivity); EC ext (electrical conductivity of the saturation extract); GWR (geographically weighted regression); Nan (anaerobically incubated nitrogen); OM (soil organic matter content); P (available phosphorous); PCA (principal component analysis); PC (principal component).Citation: Peralta, N. R.; Cicore, P. L.; Marino, M. A.; Marques da Silva, J. R.; Costa, J. L. (2015). Use of geophysical survey as a predictor of the edaphic properties variability in soils used for livestock production.

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“…As crop management progresses from large-scale blanket management to precision agriculture (PA), it requires technologies that evaluate within-field variation and provide reasonable yield forecasts. Precision agriculture is the application of a set of technologies and principles for managing spatial-and temporal-variability associated with the aspects of agricultural production, in order to improve input efficiency and primarily increase the economic return of the farming system [6][7][8]. In the major agricultural regions of the world, introduction of yield monitor technologies coupled with Global Positioning Systems (GPS) made implementation of PA practical.…”

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confidence: 99%

Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

Peralta

Assefa

et al. 2016

Remote Sensing

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A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at mid-growing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran's I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran's I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision agricultural management decisions.

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Delineation of management zones to improve nitrogen management of wheat

Cited by 115 publications

References 44 publications

Assessing wheat spatial variation based on proximal and remote spectral vegetation indices and soil properties

Assessing wheat spatial variation based on proximal and remote spectral vegetation indices and soil properties

Use of geophysical survey as a predictor of the edaphic properties variability in soils used for livestock production

Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

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