Precision agriculture

Precision agriculture ’15

edited by: John V. Stafford

Precision agriculture ’15 edited by: John V. Stafford Papers presented at the 10th European Conference on Precision Agriculture Volcani Center, Israel 12-16 July 2015

Wageningen Academic P u b l i s h e r s

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EAN: 9789086862672 e-EAN: 9789086868148 ISBN: 978-90-8686-267-2 e-ISBN: 978-90-8686-814-8 DOI: 10.3920/978-90-8686-814-8 Photo cover: Izrael valley at the north of Israel First published, 2015 © Wageningen Academic Publishers The Netherlands, 2015

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An approach to delineate management zones in a durum wheat field: validation using remote sensing and yield mapping G. Buttafuoco1*, A. Castrignanò2, G. Cucci3, M. Rinaldi4 and S. Ruggieri2 1CNR – Institute for Agricultural and Forest Systems in the Mediterranean (ISAFOM), Rende (CS), Italy; [email protected] 2CRA – Research Unit for Cropping Systems in Dry Environments (SCA), Bari, Italy 3Department of Agricultural and Environmental Science, University of Bari, Bari, Italy 4CRA – Cereal Research Centre, Foggia, Italy Abstract The paper proposes a geostatistical approach for delineating management zones (MZs) based on multivariate geostatistics, and to show the use of polygon (co)kriging to compare durum wheat response among the different MZs (polygons) by taking into account the observed within-field spatial correlation. The approach was validated using an IKONOS remote sensing image and a yield map. The study site was a durum wheat field in Southern Italy. Factorial cokriging allowed identification of the first regionalized factor at the longer scale used to partition the field into contiguous zones, whereas polygon cokriging, applied to the radiometric and yield data, confirmed the effectiveness of this delineation. Keywords: management zones, polygon kriging, multivariate geostatistics, remote sensing, yield map Introduction The main goal of sustainable agriculture is to meet human requirements by making the most efficient use of non-renewable resources. In this context, agriculture is considered as a human activity system and the farm system as a decision-making unity. Precision agriculture (PA) aims to manage the spatial variation in soil to supply the actual requirements of a specific soil and crop to parts of fields rather than average needs to whole fields (Mzuku et al., 2005). PA allows decision-making and can play an important and increasing role in enhancing crop productivity while ensuring sustainability and conserving natural resources. PA requires characterization of the within-field soil spatial variation and delineation of management zones (MZs), which are defined as homogeneous sub-field regions that have similar yield-limiting factors or similar attributes affecting yield (Doerge, 1999; Khosla & Shaver, 2001). Soil variability is the result of both natural processes and management practices, acting over different spatial and temporal scales. Therefore, it is critical to characterize soil properties, both quantitatively and spatially (Castrignanò et al., 2000; Buttafuoco et al., 2010). Interactions among several biotic, abiotic and climate factors that affect crop yield make it difficult to determine these sub-field areas. Consequently, adequate techniques for data analysis are necessary to reveal important spatial relationships and to identify those factors controlling the variation in soil properties. In agricultural systems analysis, it is important to take into account the spatial association of stable structures, which is usually based on clustering. If clustering is done uniquely in the attribute space, continuity in the geographical space can be achieved only by a posteriori filtering. A common method to impose a spatial dimension on the output is to include the coordinates (x, y) of the values as variables in the clustering process together with the attribute data (Castrignanò et al., 2009). This is a simplistic approach to spatial constraint, whereas more elegant and geostatistically robust models exist (Frogbrook & Oliver, 2007), which are still based on the weighting of coordinates in the clustering. Precision agriculture ’15

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Alternatively, multivariate geostatistics (Wackernagel, 2003) uses the information from spatial relationships among variables to subdivide an agricultural field into smaller, more homogeneous units, with respect to soil physical and chemical properties, as required for the application of precision agriculture techniques. Moreover, some of the several possible factors that determine soil variation are likely to vary over a short-range (meters) action, whereas others vary at longer (thousand meters) distances. Therefore, soil properties are expected to be correlated among them in a way that is scale-dependent. Satellite remote sensing (e.g. commercial IKONOS), providing high resolution data (4 m), and yield mapping can be considered as two complementary ways to verify the actual response of a crop to the variation of within-field soil properties. Further polygon (co)kriging (Diacono et al., 2014) can be used to estimate the expected value and (co)kriging standard deviation of the crop variables for each management zone, and then to evaluate the effectiveness of zone delineation by soil attributes taking spatial correlation into account. The main objectives of this paper were: (1) to propose a geostatistical approach for delineating management zones, based on multivariate geostatistics, and (2) to show the use of polygon (co) kriging to compare the durum wheat response among the different management zones (polygons), by taking into account the observed within-field spatial correlation. An IKONOS remote sensing image and a yield map were used in order to validate the proposed approach. Materials and methods Study area The study was carried out in the experimental farm of the CRA – Cereal Research Centre, near Foggia (41° 27’ N, 15° 36’ E, 90 m above sea level), south-eastern Italy (Figure 1). The field trial was carried out on a 12 ha field cropped with rainfed durum wheat (Triticum durum Desf.). The soil is a deep silty-clay Vertisol of alluvial origin, classified as fine, mesic, Typic, Chromoxerert (Soil Survey Staff, 2010). The climate is Mediterranean, characterized by hot and dry summers with rainfall concentrated mostly in the winter months. Soil data, IKONOS image and yield data One hundred georeferenced locations (Figure 1), that covered the field evenly, were selected by a modified version of spatial simulated annealing (Castrignanò et al., 2008). Soil sampling was carried out during two growing seasons: 2005-2006 and 2006-2007. Soil samples were taken to a depth (00.20 m) in 2005-2006 and to two depths (0-0.20 m and 0.20-0.40 m) in 2006-2007.

Figure 1. Study area and sample data locations. 242


Soil samples were analyzed for many properties but, in this study, coarse sand, fine sand, clay, organic matter (OM) and available phosphorous (P) concentration were used. Coarse sand, fine sand and clay contents were determined by the sieve-pipette method (Pagliai, 1997), organic matter content by the Walkley Black method (Violante, 2000) and available phosphorous by the Olsen method (Violante, 2000). An IKONOS exploitable remote sensed image was acquired in May 2008 (Rinaldi et al., 2010) with a scene size of about 150 km2 (25×6 km). IKONOS is a multispectral sensor that records blue (445-516 nm), green (506-595 nm), red (632-698 nm) and near infrared (NIR, 757-853 nm) bands with 4 m resolution. The image was geo-referenced in the WGS 84 system (UTM zones 33N) and IKONOS bands were normalized, converting the Digital Numbers (DNs) into top of atmosphere (TOA) reflectance using the calibration coefficients and equations. The study area has a flat surface, so the effects of local surface topography were absent. Moreover, the atmospheric conditions during the acquisition were good, therefore no atmospheric correction method was applied. The field was harvested in mid-July in the crop season 2007-2008 and grain yield (Mg/ha) was normalized at 13% moisture and recorded by a John Deere combine equipped with a yield monitor system (grain mass flow and moisture sensors). The geographical coordinates of each yield measurement were recorded with a differentially collected (OMNISTAR signal) Trimble 132 receiver at 1-m accuracy. The yield data were cleaned by removing data points that differed from the field mean by more than 2.5 standard deviation. Data analysis: geostatistical and statistical procedures Variogram modelling is sensitive to strong departures from normality because a few exceptionally large outliers may contribute to very large squared differences (Wackernagel, 2003). To avoid this problem, a Gaussian approach was used which consists of transformation of the initial attributes into Gaussian variables with zero mean and unit variance. Before interpolation of a multivariate data set, joint modelling of the spatial coregionalization between the variables is required using the linear model of co-regionalization (LMC), which assumes that all variables can be described by the same set of basic spatial structures standardized to sill one (Castrignanò et al., 2000). Co-kriging was then applied to predict the selected variables at the nodes of a 4×4 m grid and the co-kriged estimates were then back transformed with the mathematical model calculated in the Gaussian transformation. Factorial co-kriging analysis (FKA) was applied (Matheron, 1982) to estimate the scale-dependent regionalized factors that summarize most of the spatial variance in the data. FKA comprises the following steps: (1) fitting a LMC; (2) analyzing the correlation structure of variables by applying principal component analysis (PCA) on each co-regionalization matrix, so that a set of orthogonal components, known as scale-dependent regionalized factors, is extracted; (3) co-kriging and mapping regionalized factors. The polygon (co)kriging was used to provide the expected value and standard deviation of the variables in each cluster (polygon) of the field partition in management zones. Polygon (co)kriging is an extension of block (co)kriging using a special neighborhood definition (Diacono et al., 2014). All statistical and geostatistical analyses were done using the software package ISATIS®, release 2014.2 (Geovariances, 2014). Results and discussion The exploratory analysis of the data revealed considerable spatial variation in soil properties at each sampling date, and generally large departures from normal distribution. The data were mostly positively skewed which indicates the existence of large values, especially for chemical variables. This justified the transformation of all the data and the successive procedures of variography and prediction were then performed on the standardized Gaussian transformed variables. Moreover, Precision agriculture ’15

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since all variables were significantly correlated, though not to the same extent, it was decided to perform a multivariate approach. An isotropic LMC was fitted with three basic structures: (1) a nugget effect; (2) a spherical model (Webster & Oliver, 2007) with range=39.89 m; and (3) a spherical model with range=90 m. The appropriateness of the LMC was evaluated with a cross-validation test by calculating the mean error and the variance of standardized error, which were close to 0 and 1, respectively. From a visual inspection of the matrix of variograms (not shown), the direct variograms (main diagonal) generally have little structure, have a large nugget effect (c0) that exceeds the spatially structured component (c) and have an upper bound. The variogram of coarse sand appears erratic, probably because the concentrations are small relative to other components of soil texture. The degree of spatial correlation between pairs of variables was evaluated by the distance from the hull of perfect (intrinsic) correlation and the corresponding cross-variogram model (Wackernagel, 2003). The spatial correlation between soil attributes was generally small for almost all pairs of variables, which may be due to the large proportion of white noise (nugget effect) relative to the total variance. The above result suggests the need to improve the sampling by collecting additional samples in those areas of the field where the variation is expected to be larger (Castrignanò et al., 2014), as shown by the following thematic maps. To reduce the number of figures, only the maps of the shallower (0-0.20 m) depth of the later sampling are shown because there was consistency among the maps of the same variable. The maps of the raw soil variables obtained by cokriging on a 4×4 m square grid, the same resolution as the IKONOS image, display some distinct spatial patterns and also reveal some degree of spatial association among the different soil attributes. The cokriged map for available phosphorous (P) (Figure 2a) is characterized by considerable variation with several hot spots randomly located over the field. However, there is a tendency for there to be larger values on the north-western border of the field. In contrast, the map of OM (Figure 2b) seems to have a stronger spatial structure with three main zones: a southern part with generally larger values, a central area with smaller ones and a more variable northern area. The spatial association between the maps of P and OM is not strong, confirming the dynamic and spatially variable nature of the chemical fertility of soil. As regards the texture components, the map of clay content (Figure 2c) shows a wide central area characterized by larger values, whereas the northern and southern areas have the smallest values. The map of coarse sand (Figure 2d) shows a distinct wide central transverse strip with the maximum values, corresponding to the bed of an old river that has disappeared. In contrast, the larger contents of fine sand occur in the northern part of the field. Summarising these maps, the field could be split roughly into three main zones using the field averages as reference: the central area characterized by higher contents of coarse sand and clay, but

Figure 2. Cokriged maps of available phosphorous (A), organic matter (B), clay (C) and coarse sand (D) contents. 244


lower content of OM, the northern part with the highest content of fine sand and the southern part where the smallest values of all variables occur with the exception of OM and P. From the factor analysis, only the first factor at the longer range (90 m; F1) with an eigenvalue >1 was retained, because it could produce a delineation of the field into areas of size manageable by a farmer. The F1 was negatively correlated with OM and fine sand contents in both years and positively correlated with clay and coarser sand contents in both years, which also means that the main structures of spatial dependence are persistent over time. The map of the first factor (Figure 3a), displayed using three iso-frequency classes, shows a wide central area with lower content of OM up to 0-40 cm depth and higher contents of clay and coarser sand, whereas the northern and southern areas are characterized by higher contents of fine sand and organic matter, respectively. The F1 could then be assumed to be an indicator of soil organic fertility and texture and a useful tool to direct site-specific fertilizer application in durum wheat cultivation. To assess whether the method, based on a combined use of only soil data without any direct sampling of plant properties, could characterize the spatial variation of the durum wheat crop under agricultural management commonly used in the study area, the F1 map was compared with the one of NDVI obtained using the reflectance data of the four bands of IKONOS image, and the one of yield data recorded at the harvest of 2007-2008 season. To make the three (F1, NDVI and yield) maps comparable, the grain data were interpolated on the same grid as the IKONOS image. The yield data were firstly transformed to a normal distribution; a variogram model was then fitted to the experimental variogram of the Gaussian transformed data, including three basic structures: a nugget effect, an exponential model (Webster & Oliver, 2007) with range of 33 m and an exponential model with range of 90 m. The data were interpolated using ordinary kriging and finally back-transformed to the raw data. The yield map (Figure 3b) looks quite variable with several spots characterized by different potentials for production. However, the northern and eastern part of the field appear more productive. The NDVI map (Figure 3c), assumed to be a standard indicator of crop vigour, shows some similarity with the previous map with a wide central area characterized by less luxuriant vegetation compared with that in the northern and south eastern parts of the field. To make the comparison of the F1 map with the NDVI and yield maps more objective, the expected value and standard deviation of the two crop variables were calculated for each of the 14 polygons (Figure 4) of the delineation produced by F1. Figure 5a and 5b gives the results of these calculations for NDVI and grain yield, respectively. As for the former variable, the polygons can be split into three statistically different groups: the polygons 7 and 9 are the most luxuriant, the central polygons 5 and 8 the least, whereas the remaining polygons show intermediate properties.

Figure 3. Maps of the first factor at longer range (90 m) (A); crop yield (B); and NDVI (C).


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Figure 4. Delineation of polygons produced using the map of the first factor.

Figure 5. Expected value (solid circle) and standard deviation (dash) of NDVI (A) and grain yield (B) for each of the 14 polygons (Figure 4) of the delineation produced by the first factor. For grain yield, the polygons can be subdivided into two statistically different groups: the one including the polygons 2, 3, 6 and 7 is the more productive. Therefore, grain yield showed less sensitivity to soil variation compared with wheat reflectance as expressed by NDVI. Conclusions A multivariate geostatistical approach enabled the delineation of an agricultural field, cropped with durum wheat under a Mediterranean climate, into three statistically significant zones with different crop vigour or two zones with different potential productivity. Since the partition of this field relates to differences in organic fertility and particle size distribution of the soil, it can be used as a tool for directing site-specific management. Although most of the variation in rainfed wheat in southern Mediterranean regions is affected by the meteorological pattern, in particular by rainfall, nevertheless this research has proved that soil also partly controls wheat response in terms of plant vigour and production. Therefore, the variation in both (soil and plant) components of a cropping system should be taken into account for an effective management of rainfed durum wheat in precision agriculture.

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The results are encouraging and the proposed approach could be used also to test the effects of site-specific management over time. References Buttafuoco, G., Castrignanò, A., Colecchia, A.S. and Ricca, N. 2010. Delineation of Management Zones using Soil Properties and a Multivariate Geostatistical Approach. Italian Journal of Agronomy 5 323-332. Castrignanò, A., Barca, E., Buttafuoco, G., De Benedetto, D., Palumbo, D.A., Passarella, G. 2014. Integration of EMI sensor data in soil sampling scheme optimization using continuous simulated annealing. In: geoEnv 2014: Proceedings of the 10th Conference on Geostatistics for Environmental Applications, N. Jeannèe and T. Romary (Eds.), Presses des Mines, collection Sciences de la Terre, Paris, France, pp. 34-35. Castrignanò, A., Buttafuoco, G., Troccoli, A., Colecchia, S., Di Bitello, V., Pisante et al. 2008. Multivariate geostatistical analysis for delineation of management zones using crop index. In: Proceedings of EuroAgeng 2008 23th-25th June 2008, Hersonissos, Crete, Greece. CD ROM. Castrignanò, A., Giugliarini, L., Risaliti, R. and Martinelli, N. 2000. Study of spatial relationships among some soil physico-chemical properties of a field in central Italy using multivariate geostatistics. Geoderma, 97, 39-60. Castrignano, A., Guastaferro, F., De Benedetto, D., Moneta, A., Basso, B., Troccoli, A. et al. 2009. Delineation of site-specific management zones using geostatistics and fuzzy clustering analysis. In: van Henton, E.J., Goense, D., Lokhorst, C. (Eds.), Precision Agriculture ‘09. Proceedings of the 7th ECPA. Wageningen Academic Publishers, The Netherlands, pp. 537-544. Diacono, M., Castrignanò, A., Vitti, C., Stellacci, A.M., Marino, L., Cocozza, C. et al. 2014. An approach for assessing the effects of site-specific fertilization on crop growth and yield of durum wheat in organic agriculture. Precision Agriculture 15 479-498. Doerge, T.A. 1999. Management zone concepts (Online). Available at http://www.ipni.net/. Frogbrook, Z.L., Oliver, M.A. 2007. Identifying management zones in agricultural fields using spatially constrained classification of soil and ancillary data. Soil Use & Management 23 40-51. Geovariances. 2014. ISATIS Software: Technical References Release 2014.2. Geovariances & Ecole des Mines de Paris: Paris, France, 220 pp. Khosla, R., Shaver, T. 2001. Zoning in on nitrogen needs. Colorado State University Agronomy Newsletter 21 24-26. Matheron, G. 1982. Pour une analyse krigeante des données régionalisées (For a kriging analysis of regionalised data). Report N-732, Centro de Géostatistiques, École des Mines de Paris, Fontainebleau, France. Mzuku, M., Khosla, R., Reich, R., Inman, D., Smith, F. and MacDonald, L. 2005. Spatial Variability of Measured Soil Properties across Site-Specific Management Zones. Soil Science Society of America Journal 69 1572-1579. Pagliai, M. (Ed.) 1997. Metodi di Analisi Fisica del Suolo (Physical Methods of Soil Analysis), Italian Ministry of Agriculture, Franco Angeli, Milan (in Italian), Italy. Rinaldi, M., Ruggieri, S., Garofalo, P., Vonella, A.V., Satalino, G. and Soldo, P. 2010. Leaf Area Index Retrieval Using High Resolution Remote Sensing Data. Italian Journal of Agronomy 5 155-166. Soil Survey Staff. 2010. Keys to Soil Taxonomy, 11th edit., USDA – US Department of Agriculture, Natural Resources Conservation Service, Washington, DC. Violante, P. (Ed.) 2000. Metodi di Analisi Chimica del Suolo (Chemical Methods of Soil Analysis). Italian Ministry of Agriculture. Franco Angeli, Milan (in Italian), Italy. Wackernagel, H. 2003. Multivariate Geostatistics: an introduction with applications. 3rd ed. Springer-Verlag: Berlin, Germany 388 pp. Webster, R., Oliver, M.A. 2007. Geostatistics for Environmental Scientists. 2nd ed., Wiley: Chichester, UK, 330 pp.


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