Spatial and temporal variability of wheat grain yield and quality in a Mediterranean environment: A multivariate geostatistical approach

Field Crops Research 131 (2012) 49–62

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Spatial and temporal variability of wheat grain yield and quality in a Mediterranean environment: A multivariate geostatistical approach Mariangela Diacono a,∗ , Annamaria Castrignanò b , Antonio Troccoli c , Daniela De Benedetto b , Bruno Basso d , Pietro Rubino a a

Department of Agri–Environmental and Land Sciences, University of Bari, Via Amendola 165/a, 70126 Bari, Italy CRA – SCA, Research Unit for Cropping Systems in Dry Environments, Via C. Ulpiani 5, 70125 Bari, Italy c CRA – CER, Cereals Research Centre, S.S. 16, km 675, 71122 Foggia, Italy d Department of Crop, Forest and Environmental Sciences, University of Basilicata, Viale Ateneo Lucano 10, 85100 Potenza, Italy b

a r t i c l e

i n f o

Article history: Received 13 July 2011 Received in revised form 2 March 2012 Accepted 5 March 2012 Keywords: Rainfed durum wheat Factorial co-Kriging Analysis Management classes Temporal stability

a b s t r a c t In Mediterranean countries, on top of the erratic weather pattern, rainfed wheat grain yield and protein content in a field are spatially variable due to inherent variability of soil properties and position in the landscape. The objectives of this three-year field study were: (i) to assess the spatial and temporal variability of attributes related to the yield and quality of durum wheat production; and, (ii) to examine the temporal stability of sub-field management classes derived from (i). A Geostatistical approach was used to analyze data collected in each year from 100 georeferenced locations. In particular, block-kriging was used to produce maps of gluten and protein content, test weight, biomass weight and Harvest Index. The multivariate spatial data sets were then analyzed by Factorial co-Kriging Analysis (FKA). The classes obtained from the FKA output were compared with the yield maps in order to assess their production potential. The first factors relating to each year were also compared by using contingency matrices, to estimate the temporal consistency of field delineation. In the first two seasons, at most, about 50% of the total spatial variance of the crop attributes was ascribed to production potential. In the third season the variation was more erratic, equally influenced by all variables. The contingency matrices have showed that only 26% on average of the spatial variation of the attributes of wheat production was characterized by temporal stability. The present study highlighted the influence of climatic conditions over the persistence of wheat crop responses. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Crop yield is a result of the interaction between plant genetic traits, soil properties, field management and climatic conditions. In a Mediterranean environment, variation in rainfed durum wheat yield is mainly caused by the erratic weather pattern, characterized by low and irregular rainfall distribution and high temperatures during the ‘grain filling’ stage (Basso et al., 2010; Troccoli et al., 2000). Moreover, spatial and temporal variability of soil properties

Abbreviations: PA, Precision Agriculture; FKA, Factorial co-Kriging Analysis; LMC, Linear Model of Co-regionalization; , simple Kappa coefficient; CV, coefficient of variation; HI, Harvest Index; d.m., dry matter. ∗ Corresponding author. Tel.: +39 080 5443004; fax: +39 080 5442976. E-mail addresses: [email protected] (M. Diacono), [email protected] (A. Castrignanò), [email protected] (A. Troccoli), [email protected] (B. Basso), [email protected] (P. Rubino). 0378-4290/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.fcr.2012.03.004

may affect wheat growth, yield and grain quality at a within-field scale. The magnitude and structure of this within-field variability may suggest the suitability of site-specific management (Mzuku et al., 2005; Godwin and Miller, 2003), which aims to increase both profitability and environment protection, by reducing the risk of pollution from chemical inputs applied at levels greater than the optimum (Basso et al., 2009, 2011; Di Fonzo et al., 2001). Previous research has demonstrated that the combined analysis of soil and crop-growth parameters can be effective in delineating areas of different yield potential (Basso et al., 2011; Fleming et al., 2004; Taylor et al., 2003). Soil surveys have traditionally given indications of crop productivity potential, but the advent of Precision Agriculture (PA) has provided a more accurate and fine-resolution assessment of spatial variation (Stafford and Bolam, 1998; Stafford, 2000; Vrindts et al., 2003). Variography, defined as the process of modelling spatial variation, is commonly used in PA to assess the spatial dependence of soil and crop attributes. It is based on the concept that values

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observed at closely spaced locations are more similar than those from further apart (Isaaks and Srivastava, 1989; Legendre, 1993; Fortin et al., 2002). There is a further need to establish evidence of the link between the spatial autocorrelation of crop attributes and the opportunity for spatial agronomy. One approach for this can be to delineate management classes within a field (Taylor et al., 2007). Within a management class, more or less uniform effects of weather, soil and farm management are expected on the crop (Castrignanò et al., 2008; Fridgen et al., 2004; Lark, 1998). The delineated management classes may form the basis for spatially variable application of inputs (e.g. fertilizers), once the factors responsible for the variability have been identified. Given the complexity of the interactions among the factors that affect grain yield and quality, a multivariate approach to the determination of management classes is advisable. Several classification techniques are reported in scientific literature, among which there are those based on minimization of within-unit variance (Fraisse et al., 2001; Silverman, 1986; Stafford et al., 1998) and non-parametric techniques based on multivariate density estimation (Guastaferro et al., 2010). The multivariate approach also applies to geostatistical analysis when the relationships among the variables are scale-dependent and can be related to different sources of variability. The approach used, called Factorial co-Kriging Analysis (FKA) (Matheron, 1982), is a combination of traditional Principal Component Analysis, for describing the correlation of a multivariate dataset, and geostatistics to take into account the regionalized nature of the variables. It uses the information arising from both relationships among variables and spatial dependence between observations, for a synthetic representation of spatial variation which can aid in delineating the class boundaries (Castrignanò et al., 2000). Some factors that affect crop response might have a short-range action, whereas others operate at longer distances, therefore the delineation of homogeneous areas should be scale-dependent. An important advantage of FKA is the possibility of extracting a restricted number of regionalized scale-dependent factors which could be used to support decision making in PA (Casa and Castrignanò, 2008). For these reasons FKA can be a useful technique for delineating management classes. The novelty of the present work is the application of a combined statistical and geostatistical approach for various durum wheat yield properties, and to define the spatial dependence among them, as well as their temporal stability. There are few papers, at present, on the application of multivariate geostatistical analysis to spatial and temporal wheat yield and grain quality data. Thus, the objectives of this three-year research, carried out in a Mediterranean environment, were: (i) to assess the spatial and temporal variability of attributes related to the yield and quality of durum wheat production; and (ii) to examine the temporal stability of sub-field management classes which derived from (i).

2. Materials and methods 2.1. Site description, sampling and measurements The research was carried out at the Experimental Farm of the Cereals Research Centre of the Italian Agricultural Research Council (CRA-CER), Foggia, southern Italy (41◦ 27 N, 15◦ 36 E, 90 m a.s.l.) during the 2005–2006, 2006–2007 and 2007–2008 crop seasons (from now on indicated as 0506, 0607 and 0708, respectively). The trial was conducted on a 12-ha field, cropped with rainfed durum wheat (Triticum durum Desf. cv “Gargano”), in continuous cultivation. The soil, typical of the Apulian Tavoliere of south-eastern Italy, is a silty-clay Vertisol of alluvial origin, classified as Fine, Mesic, Typic

Chromoxerert by Soil Taxonomy-USDA (Soil Survey Staff, 1999). The climatic conditions are generally those of a typical Mediterranean environment, characterized by a dry season between May and September and a cold season from October-November to March-April. In the trial area, vegetative and reproductive stages of wheat normally occur from mid-November to March and from April to June-July, respectively. In Fig. 1 the monthly mean temperatures and the rainfall for each wheat cropping season were compared with the long-term averages (1952–2004). The rainfall during November to July was higher in 0506 (480 mm) and 0708 (345 mm), than in 0607 (330 mm). Moreover, 0607 and 0708 showed lower rainfall than the long-term 53-year average of 415 mm. Over the three-year period the average maximum and minimum temperatures of the wheat growing season were slightly higher than the long-term average. Also, the averaged maximum temperature was higher in 0607 by 2.7, 5.8 and 10.2%, compared to that of 0506, 0708 and 53-year period, respectively, during the critical phase of grain maturation (April–June). Prior to the 0506 and 0607 growing seasons the soil was ploughed to 30-cm depth, followed by disk harrowing for seedbed preparation. In 0708 direct seeding on no-tilled soil was preferred due to the continuous heavy rainfall from mid-November to midDecember that hindered the normal tillage practices. A glyphosate herbicide was applied ten days before sowing. The crop was sowed in the second half of November in 0506 and 0607, and in the midDecember of 0708. Nitrogen fertilizer (90 kg N ha−1 ) was applied in two split applications: 1/3 N as diammonium phosphate before sowing and 2/3 N as ammonium nitrate at tillering, corresponding to Stage 20 of the Zadoks scale (Zadoks et al., 1974). One hundred georeferenced locations were selected within the field. The total number of samples was dictated by financial constraints and based on requirements of estimate accuracy. Approximately ten samples per hectare were deemed acceptable. The locations of the sampling points were chosen so that they evenly covered the field (Castrignanò et al., 2008) by using the spatial simulated annealing algorithm of van Groenigen et al. (1999), modified to allow the inclusion of objective weighting factors by using auxiliary information. In particular, an orthophoto of the area recorded in a previous early spring (data not shown) was used, and the gradient of grey level employed as auxiliary information to optimize the spatial sampling of field. The separation distance between the sampled locations was generally 30–40 m. Measurements of hand-harvested variables were repeated at the 100 georeferenced locations in each year. Five aboveground samples of 1 m2 of wheat plants were collected from every location. Test weight (also referred as hectoliter weight, kg hL−1 ), total protein of grain (% dry matter, d.m.) and content of the gluten fraction of total protein (% d.m.) were averaged from the five samples and automatically determined by Near Infrared Transmission spectroscopy, using an InfratecTM 1229 Grain Analyzer (Foss, Italy). Fertile plants m−2 , total biomass weight (g m−2 ) and grain weight (g m−2 ) were averaged from only four out of the five samples collected at the 100 locations in each cropping season. The last two variables were used to calculate the Harvest Index (HI, %). Thirty plants were randomly taken from the fifth sample, in the first two years of the trial, to measure yield components such as ear length (cm), weight of kernels (g), number of kernels and number of spikelet per ear. 2.1.1. Yield monitoring The field was harvested in late June in 0506 and mid-July in 0607 and 0708. Grain yield (t ha−1 ) was normalized at 13% moisture. Yield data were recorded by a John Deere combine equipped with a yield monitor system (grain mass flow and moisture sensors) in 0506 and 0708. Since the John Deere combine was unavailable in 0607, the crop was harvested by using a smaller common combine harvester. In 0607, in order to better localize the data collection,

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Fig. 1. Mean monthly temperature and rainfall at the study site in each growing season, compared with long-term values (1952–2004).

776 elementary plots of 24 m2 (1.5 m × 16 m) were drawn in the field, parallel to the longer side, in continuous strips and at 8 m away from each other. The geographical coordinates of each yield measurement were recorded with a differentially corrected (OMNISTAR signal) Trimble 132 receiver, with 1-m accuracy, in 0506 and 0708, and by referring to the middle points of the elementary plots in 0607. The yield data were cleaned by removing data points that differed from the field mean by >2.5 standard deviations. 2.2. Statistical and geostatistical analysis Data analyses were performed separately for each season. The yield-monitored data were submitted to univariate analysis, whereas for hand-harvested variables a multivariate analysis was preferred, due to the correlations existing among these variables. The geostatistical analyses were performed with ISATIS (Geovariances, release 10.04, 2010). Variogram modeling is sensitive to strong departures from normality, because a few exceptionally large values may contribute to very large squared differences. Therefore, multiGaussian coKriging was used to produce maps of the hand-harvested and the yield-monitored variables. To ensure that each variable had a Gaussian distribution, we first applied “Gaussian anamorphosis”, which consists of determining a mathematical function to transform a variable with ‘any’ distribution into a standardized Gaussian distribution (Wackernagel, 2003). This transformation is made by using an expansion of Hermite polynomials (Wackernagel, 2003) restricted to a finite number (30–100) of terms. All the successive procedures of variography and estimation were performed on the transformed Gaussian variables. A Linear Model of Co-regionalization (LMC), including a nugget effect and a double-spherical (for 0506) or a spherical (for 0607 and 0708) model, was fitted to all experimental auto and cross-variograms of the Gaussian-transformed, hand-harvested variables. On the other hand, a variogram model was fitted to the experimental direct semi-variogram of the Gaussian transformed yield for each harvest. All hand-harvested and yield-monitor variables were interpolated at the nodes of a 12 m × 12 m grid by using ordinary block-kriging. The back-transformed block-kriging estimates were then mapped. We tested the goodness of fitting by using cross-validation. In particular, we calculated two statistics: mean error and average of the

squared standardized errors (Wackernagel, 2003). The two values should be close to zero and one, respectively. The field in each map was classified into three management classes (low, medium and high) by splitting the overall range of variation through the 33 and 66% quantiles. For the yield-monitor variables, this was straightforward because they were considered univariate. For the hand-harvested variables, a data reduction technique was first needed to describe the multivariate variation. To this end we used FKA, the basic steps of which are: (i) analyzing the correlation structure between the variables, by applying Principal Component Analysis independently on each spatial structure of the LMC; (ii) co-Kriging specific factors at each characteristic spatial scale and mapping them (Wackernagel, 2003). For the purpose of this study, we only implemented (i) on the autocorrelated structure of the LMC, as the nugget effect comprises high-frequency, unmanageable variation. According to the Kaiser criterion (Kaiser, 1960), we retained only the regionalized factors corresponding to eigenvalues greater than one, to aid in delineating the field into management classes of such a size to be manageable by farmer. In each season it was sufficient to retain only the first regionalized factor (denoted herein F1). The long range F1 for 0506 was used, because the short- range F1 for this season produced a delineation in homogeneous zones of such a small size that it is difficult to manage in practice. The comparison between regionalized factor and yield for each year was quantitatively performed through twoway contingency matrices, where each cell gives the frequency, the overall percentage, the percentage per row and the percentage per column. The percentage of each category that was “correctly” classified was determined (Campbell, 2002). The overall accuracy, which is a measure of the spatial agreement between the two types of maps, was also computed as the proportion of the total number of observations along the main diagonal of the contingency matrix. Moreover, the regionalized factors were compared between years, through contingency matrices, to estimate the temporal consistency of field delineation. We computed Bowker’s test of symmetry (Bowker, 1948), for which the null hypothesis is that the percentages are symmetric for all pairs of cells, which implies marginal homogeneity. The simple kappa coefficient () introduced by Cohen (1960) was also used. This coefficient measures the inter-classification agreement that, in the case of independent ratings of the same subjects, equals 0 when

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Table 1 Descriptive statistics of durum wheat yield and quality parameters for each trial year (2005–2006, 2006–2007 and 2007–2008 crop seasons are indicated as: 0506, 0607 and 0708, respectively). Variable

Units

Min

Max

Mean

Std. dev.

CV

Skewness

Kurtosis

Gluten 0506 Gluten 0607 Gluten 0708 Protein 0506 Protein 0607 Protein 0708 Test weight 0506 Test weight 0607 Test weight 0708 Grain weight 0506 Grain weight 0607 Grain weight 0708 Biomass weight 0506 Biomass weight 0607 Biomass weight 0708 HI 0506 HI 0607 HI 0708 Fertile plants 0506 Fertile plants 0607 Fertile plants 0708 Ear length 0506 Ear length 0607 Kernel weight 0506 Kernel weight 0607 Kernel number 0506 Kernel number 0607 Spikelet number 0506 Spikelet number 0607

% d.m. % d.m. % d.m. % d.m. % d.m. % d.m. kg hL−1 kg hL−1 kg hL−1 g m−2 g m−2 g m−2 g m−2 g m−2 g m−2 % % % n m−2 n m−2 n m−2 cm cm g g n n n ear−1 n ear−1

6.70 4.60 7.40 11.70 9.60 10.30 74.55 60.20 73.70 109.24 21.20 40.00 424.00 140.00 235.00 17.28 12.60 6.77 138.00 34.00 80.00 3.77 4.09 0.47 0.42 12.30 13.63 12.23 10.67

12.90 13.80 11.90 16.20 18.20 15.40 84.85 78.10 81.60 450.44 274.00 445.50 1280.00 800.00 1336.00 45.13 47.38 39.23 486.00 297.00 830.00 6.33 6.62 1.98 2.97 34.43 50.60 20.67 19.60

10.88 7.78 9.22 14.20 12.34 12.49 79.82 70.86 78.62 250.50 123.04 247.05 857.37 464.54 899.04 28.97 25.99 27.04 228.28 160.11 204.90 5.49 5.10 1.34 1.06 25.46 26.96 17.76 15.23

0.98 1.57 0.91 0.96 1.52 0.99 1.79 3.84 1.35 69.59 56.72 83.29 178.24 164.40 229.72 4.17 6.52 4.77 50.87 54.82 83.57 0.50 0.48 0.31 0.34 4.63 5.74 2.21 1.65

0.09 0.20 0.10 0.07 0.12 0.08 0.02 0.05 0.02 0.28 0.46 0.34 0.21 0.35 0.26 0.14 0.25 0.18 0.22 0.34 0.41 0.09 0.09 0.23 0.32 0.18 0.21 0.12 0.11

−0.84 0.80 0.52 −0.25 0.69 0.62 −0.22 −0.51 −0.77 0.26 0.28 −0.31 0.07 0.14 −0.41 −0.09 −0.24 −1.14 1.63 0.38 4.29 −1.02 0.24 −0.62 1.69 −0.43 0.63 −0.68 0.18

5.38 4.19 3.13 2.86 3.86 3.33 3.17 2.80 4.15 2.81 2.49 2.63 2.64 2.37 2.87 4.86 5.18 5.84 8.28 3.10 32.68 3.84 3.09 3.54 10.94 2.83 4.76 2.40 3.05

Note. d.m., dry matter.

the agreement is due to chance alone, +1 when there is complete agreement, and −1 when there is complete disagreement. In addition to the coefficient, its confidence limits were also calculated. The approach was implemented with the FREQ procedure of the SAS/STAT software package (SAS/STAT Software Release 9.2, 2010). 3. Results 3.1. Spatial within-field variability of yield and qualitative parameters of durum wheat and persistence over time Table 1 summarizes the descriptive statistics of the studied variables. Substantial variability can be detected within the field for some variables, as shown by the coefficient of variation (CV). This variability was noticeable for biomass, grain and kernel weights, as well as number of fertile plants during the entire three-year period. There was a substantial variation in HI, ranging from a minimum of 6.77% in 0708 to a maximum of 47.38% in 0607. However, the HI mean value was lower in 0607 (25.99%) than both in 0708 (27.04%) and 0506 (28.9%). The biomass weight ranged from a minimum of 140 g m−2 in 0607 to a maximum of 1336 g m−2 in 0708; grain weight ranged from a minimum of 21.2 g m−2 in 0607 to a maximum of 450.44 g m−2 in 0506, respectively, and fertile plants from a minimum of 34 in 0607 to a maximum of 830 in 0708. The data distributions were generally skewed, either positively or negatively, in some cases with large deviations from the normal distribution, such as for HI in 0708, fertile plants in 0506 and 0708, ear length in 0506 and kernel weight in 0607. The variability of the hand-harvested variables was probably due to differences in total precipitation during the growing season, from year to year. This was particularly evident for the production parameters, which showed lower values in the driest wheat growth season 0607. In fact, in 0607 there occurred: (i) before sowing, between September and November, rainfall lower by 11.3, 37.6 and 29.2% than in 0506, 0708 and the 53-year period, respectively;

(ii) during the vegetative cycle, rainfall lower by 39.4% than in 0506; (iii) the highest mean maximum temperature (28.5 ◦ C) during the reproductive cycle (April–July), as previously reported. Moreover, contrary to 0506 and 0708, fewer rainfall events occurred in 0607 after the N fertilizer applications. These particular climatic conditions limited the vegetative period in 0607 and might have reduced the duration of tillering stage, leading to significant lowering of the total biomass weight. Although the mean kernel number was slightly higher in 0607 than in 0506, in the driest season the lowest grain weight was obtained. This reduced grain weight might be due to the smaller spikelet number and reduced kernel filling, as shown by the lower test weight. The mean value of grain protein content during the three-year period was typical of medium quality wheat (about 13% d.m.). The high rainfall which occurred in 0506 after fertilizer applications might have promoted plant N uptake and translocation and, consequently, produced better-quality grain. Most variables were significantly correlated (P < 0.05; data not shown) and this result guided the choice of the multivariate data set to be submitted to the geostatistical analysis. The LMC included the following basic structures: (i) a nugget effect; (ii) a double-spherical model with ranges of 80 and 400 m in 0506, a spherical model with range of 250 m in 0607, and a spherical model with range of 90 m in 0708. These results showed how the spatial variability was well structured in 0506 with two types of processes acting at two different spatial scales, whereas it was more erratic in 0708. Intermediate results were obtained in 0607. The goodness of model fitting for all variables was generally satisfactory, because the mean error and the average of the squared standardized errors were close to zero and one, respectively. In particular, the mean error ranged between −0.0014 and 0.0086, in 0506; −0.0002 and 0.0095, in 0607, and −0.0008 and 0.0181, in 0708. The average of the squared standardized errors ranged between 1.0525 and 1.3946 in 0506; 1.0765 and 1.4747 in 0607, and 1.0266 and 1.2178 in 0708.

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Fig. 2. Block-kriged maps of grain gluten content (% dry matter) for the 2005–2006, 2006–2007 and 2007–2008 crop seasons (indicated as: 0506, 0607 and 0708, respectively).

The spatial maps of the raw variables, obtained by block-kriging, revealed some distinct spatial patterns and some degree of spatial association among them. In particular, Figs. 2–6 show the maps of the three-year period concerning some variables deemed the most relevant: three grain quality parameters (gluten, protein content and test weight) and biomass weight and HI. The maps of gluten and protein contents looked quite similar in each year. The maps of 0506 showed higher gluten and protein contents (>11 and >14% d.m., respectively) along the western boundaries. The two maps of 0607 can be divided into two main areas, i.e. the northern part characterized by both high gluten and protein values (>7.6 and >12.3% d.m., respectively) and the southern one characterized by low contents. The opposite pattern occurred in 0708 for both variables. The similarity, among the gluten and protein maps demonstrated that these quality parameters were positively spatially correlated and that this relation was persistent over time. However, the results also showed that there was no temporal consistency in the spatial patterns. In fact, the spatial distributions of both gluten and protein were so unstable that the locations of highs and lows were inverted in 0607 and 0708. This was probably due to differences in the complex interactions among seasonal climatic conditions, soil fertility characteristics and soil tillage practices. The 0708 season showed more amenable climatic conditions, such as higher rainfall before sowing and lower maximum temperature during grain filling, compared to 0607. The highest values of the test weight parameter, which is important for semolina production, were obtained in the centralsouthern area in 0506 and 0607. An inverse distribution in 0708 season was seen. As expected, the distribution of grain contents of gluten and protein suggested an inverse relationship with test

weight, especially in 0607 and 0708. Therefore, the semolina quality might be reduced in areas where potentially more can be produced. No persistence of spatial patterns was showed in the blockkriged maps of biomass weight and HI, from one year to another. In 0506 there was a tendency to higher biomass weight values in the south-eastern part of the field. In 0607 some similarity in the spatial distributions of the two parameters might be detected. The biomass weight map of 0607 can be roughly split into a southern and northeastern area with generally higher contents, and the remaining area with lower contents. Finally, in 0708 the field showed a central zone with the highest values. These patterns were quite similar to those of grain weight and the number of fertile plants during the entire trial period and for the first two seasons, respectively (data not shown). The HI maps were different from one year to another. In particular, in 0506 higher values occurred into some areas of small size localized along the western boundaries, in 0607 the spatial distribution was quite similar to that of biomass weight, whereas in 0708 higher values were found in the northern area. 3.2. Factorial co-Kriging results Table 2 shows the structure of the retained regionalized factors, with the explained variance (%) at the corresponding spatial scale. In 0506, about 69% of the variation in the yield properties at 80 m scale was related to the first regionalized factor (F1), on which gluten and protein contents, kernel number and spikelet number weighed positively. The F1 at 400-m scale explained about 78% of the variation in the yield properties and was related positively to grain and biomass weights, ear length and kernel number.

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Fig. 3. Block-kriged maps of grain protein content (% dry matter) for the 2005–2006, 2006–2007 and 2007–2008 crop seasons (indicated as: 0506, 0607 and 0708, respectively).

Fig. 4. Block-kriged maps of test weight (kg hL−1 ) for the 2005–2006, 2006–2007 and 2007–2008 crop seasons (indicated as: 0506, 0607 and 0708, respectively).

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Fig. 5. Block-kriged maps of biomass weight (g m−2 ) for the 2005–2006, 2006–2007 and 2007–2008 crop seasons (indicated as: 0506, 0607 and 0708, respectively).

Therefore, this factor could be interpreted as an indicator of production potential. In the second cropping season F1 explained approximately 71% of variance at 250 m scale and was mostly affected by HI, grain and biomass weights, fertile plants, kernel number and their weight. Finally, in 0708 even the first factor at 90 m scale had an eigenvalue less than 1, which confirmed the previous result that most variation was erratic. However, we preferred to retain F1 so to have a synthetic index to delineate the field into homogeneous zones also for 0708. Such an F1 described only about 51% of the variation. As expected, it was affected strongly by all the studied hand-harvested variables, i.e. positively by grain and biomass weights and fertile plants, and negatively, but to a lesser extent, gluten and protein contents. These results pointed to substantial differences among the crop seasons. In the first two seasons, the variation was generally better structured (longer range) and mostly affected by wheat yield and yield components (kernel number and their weight; spikelet number; fertile plants; ear length), whereas

in 0708 the variation was at a very short range and approximately equally affected by all variables. This was quite likely due to contingent causes that occurred in 0708, such as a lack of emergence, which were not investigated in this paper. The differences among the retained regionalized factors in the three seasons can also be detected in Fig. 7. It shows the maps of the F1 for each trial year classified into three nominal classes of equal frequency as explained in Section 2.2 of this paper. The map (Fig. 7a) of F1 (range = 80 m) for the first season showed relatively low values along the eastern part and relatively high values in an intricate pattern on the opposite side. The other F1 map (Fig. 7b) in 0506 (range = 400 m) highlighted a middle-south part with the highest values, and the northeastern and extreme southern areas with lower ones. The F1 map (Fig. 7c) of the second season (range = 250 m) showed an inverse pattern, as compared to the previous season, whereas in 0708 (Fig. 7d) a more erratic variability (range = 90 m) was observed.

Table 2 Loading values of the first regionalized factors, corresponding eigen-values and explained variance (%) at each spatial scale (2005–2006, 2006–2007 and 2007–2008 crop seasons are indicated as: 0506, 0607 and 0708, respectively). All variables were Gaussian transformed.

Gluten Protein Test weight Grain weight Biomass weight HI Fertile plants Ear length Kernel weight Kernel number Spikelet number Eigen Val. Var. Perc.

0506 F1 (400 m)

0506 F1 (80 m)

0607 F1 (250 m)

0708 F1 (90 m)

0.078 0.150 0.191 0.460 0.513 0.221 0.213 0.357 0.317 0.351 0.111 2.324 77.54

0.517 0.526 −0.042 0.137 0.091 0.181 −0.125 0.312 0.202 0.325 0.364 2.271 68.70

0.032 0.028 −0.056 0.481 0.388 0.346 0.319 0.262 0.465 0.325 −0.038 1.687 70.930

−0.315 −0.315 0.256 0.319 0.523 −0.271 0.537 – – – – 0.567 51.130

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Fig. 6. Block-kriged maps of HI (%) for the 2005–2006, 2006–2007 and 2007–2008 crop seasons (indicated as: 0506, 0607 and 0708, respectively).

3.3. Spatial distribution of durum wheat yield-monitor data The mean grain yield was 3.1 t ha−1 in the first trial year, 1.1 t ha−1 in the second season and 2.9 t ha−1 in the third. The spatial distribution of grain yield (classified by quantiles) also differed from year to year (Fig. 8). In 0506 (Fig. 8a) the highest yield values occurred in the central-southern part of the field and the lowest in the north-eastern and southern areas. In the second season (Fig. 8b) the yield map appeared more smoothed, due to the different mode of harvesting, and can be roughly split into an eastern side, generally characterized by higher productivity, and a western side with lower values. Finally, the 0708 map (Fig. 8c) confirmed a high erratic variability, even if the western side was less productive. 3.4. Validation of homogeneous field areas The classified yield maps, above described, appeared visually comparable with those of the homogeneous zones obtained by FKA. In particular, in 0506 there was a direct correspondence between the lowest (or the highest) F1 (range = 400 m) values and the lowest (or the highest) yield classes. This kind of correspondence was also observed in 0607, even if it was less evident. In 0708 there was a different spatial pattern between the F1 and the yield maps, quite likely due to a more erratic distribution. We suppose that in

0708 the F1 was influenced by different sources of variation such as properties of soil, crop emergence and stress conditions and the complex interactions among all such factors. Table 3 shows the association between the F1 classes and the yield classes over the three growing seasons. The overall accuracy was 53, 53 and 45% for 0506, 0607 and 0708, respectively. The Bowker’s test of symmetry showed that the hypothesis of homogeneity between the two maps cannot be accepted. In 0506 and 0607 the  coefficient, although significantly different from 0, showed low values indicative of a poor spatial agreement. Also, the confidence intervals suggested that the true  values were greater than zero. Since the medium class is a transition class, the high and low classes can be highlighted. The percentage of the high class for F1 corresponded to about 59, 60 and 34% of the high class for yield in the three crop seasons (Table 3a, b and c, respectively). The percentage of the low class for F1 corresponded to about 57, 45, and 36% of the low class for yield in the three crop seasons (Table 3a, b and c, respectively). These results showed that only about 50% of the total spatial variance, described by the regionalized factors, can be ascribed to production potential in 0506 and 0607. The delineation of the field using the regionalized factors takes into account other quantitative and qualitative components of production, besides the yield. In the 0708 there was the highest randomness in spatial variation, as previously observed, with less than 40% of the total spatial variance described by F1 reflected in yield variation. This

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Table 3 Contingency tables between regionalized F1 classes and yield classes in 2005–2006 (a), 2006–2007 (b) and 2007–2008 (c) crop seasons (indicated as: 0506, 0607 and 0708, respectively). Each cell gives the frequency, percentage of the total frequency, the row percentage and the column percentage. (a) clF0506a

h

l

m

Total

clyield0506d h

l

m

Total

139 19.17 59.40 56.97 42 5.79 16.73 17.21 63 8.69 26.25 25.82 244 33.66

27 3.72 11.54 11.20 143 19.72 56.97 59.34 71 9.79 29.58 29.46 241 33.24

68 9.38 29.06 28.33 66 9.10 26.29 27.50 106 14.62 44.17 44.17 240 33.10

234 32.28

251 34.62

240 33.10

725 100.00

(a-a) S

3.6342

 95% Lower 95% Upper (b)

0.3028 0.2484 0.3572

clF0607b

h

l

m

Total

Pr > S

0.3038

clyield0607e h

l

m

Total

144 19.86 60.25 60.76 41 5.66 16.47 17.30 52 7.17 21.94 21.94 237 32.69

59 8.14 24.69 26.22 111 15.31 44.58 49.33 55 7.59 23.21 24.44 225 31.03

36 4.97 15.06 13.69 97 13.38 38.96 36.88 130 17.93 54.85 49.43 263 36.28

239 32.97

249 34.34

237 32.69

725 100.00

(b-b) S

17.7544

 95% Lower 95% Upper

Pr > S

0.0005

0.2970 0.2426 0.3514

(c) clF0708c

h

l

m

Total

clyield0708f h

l

m

Total

81 11.17 33.89 33.89 67 9.24 26.59 28.03 91 12.55 38.89 38.08 239 32.97

67 9.24 28.03 28.39 92 12.69 36.51 38.98 77 10.62 32.91 32.63 236 32.55

91 12.55 38.08 36.40 93 12.83 36.90 37.20 66 9.10 28.21 26.40 250 34.48

239 32.97

252 34.76

234 32.28

725 100.00

(c-c) S  95% Lower 95% Upper

1.5059

Pr > S

0.6809

−0.0052 −0.0565 0.0461

Note. a, b, c = first factor classes for 2005–2006 (0506), 2006–2007 (0607) and 2007–2008 (0708) crop seasons, respectively; d, e, f = yield classes for 0506, 0607 and 0708, respectively; h, high; m, medium; l, low; S, statistic by test of symmetry; , kappa coefficient; 95% Lower and Upper,  confidence limits.

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M. Diacono et al. / Field Crops Research 131 (2012) 49–62

Fig. 7. (a–d) Classification of F1 for each crop season, obtained by splitting the overall range of variation into three equal quantiles: (a) F1 2005–2006 with range of 80 m; (b) F1 2005–2006 with range of 400 m; (c) F1 2006–2007; and (d) F1 2007–2008.

result might be attributed to a large degree of uncertainty resulting from different climatic conditions, to N losses into the atmosphere, and to the soil fertility deterioration during continuous wheat cultivation. Table 4 presents the temporal consistency of the classifications of F1. More than 66% of the high class shifted to the low class from

0506 to 0607. Conversely, about 71% of the low class was transformed in high one, from 0506 to 0607. The reversal of trend may be explained by taking into account the climatic differences in the two seasons. Such a reversal was less evident in the passage from 0607 to 0708, when more than 45% shifted from the high to the low class and about 35% from low to high, respectively.

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Fig. 8. (a–c) Classification of yields for each crop season, obtained by splitting the overall range of variation into three equal quantiles: (a) yield 2005–2006; (b) yield 2006–2007 and (c) yield 2007–2008.

The overall persistence, regarded as the percentage of locations mapped to the same class in more than one season, was about 21 and 31% for the first and the second comparison, respectively. This means that there was generally a very high temporal variation between the seasons. The Bowker’s test of symmetry showed that the hypothesis of homogeneity between the two successive maps cannot be accepted. The  coefficients were negative, showing disagreement. Therefore, the results showed that only 26% on average of the total spatial variance, described by the regionalized factors, was temporally consistent.

4. Discussion 4.1. Effects of spatial and temporal variability of wheat yield and delineation of homogeneous areas There is a need to control the interactions between environmental factors and farming practices to gain high yields and good quality of durum wheat in Mediterranean environments. From this perspective, the assessment of within-field variation is assumed to form the basis for variable rate application of agronomic inputs.

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Table 4 Contingency tables between 2005–2006 (0506) and 2006–2007 (0607) F1 classes (a), and 2006–2007 (0607) and 2007–2008 (0708) F1 classes (b). Each cell gives the frequency, percentage of the total frequency, the row percentage and the column percentage. (a) clF0506a

h

l

m

Total

clF0607b h

l

m

Total

7 0.97 2.99 2.93 178 24.55 70.92 74.48 54 7.45 22.50 22.59 239 32.97

156 21.52 66.67 62.65 24 3.31 9.56 9.64 69 9.52 28.75 27.71 249 34.34

71 9.79 30.34 29.96 49 6.76 19.52 20.68 117 16.14 48.75 49.37 237 32.69

234 32.28

251 34.62

240 33.10

725 100.00

(a-a) S

7.1509

 95% Lower 95% Upper (b) clF0607b

h

l

m

Total

Pr > S

0.0672

−0.1941 −0.2384 −0.1498 clF0708c h

l

m

Total

70 9.66 29.29 29.29 88 12.14 35.34 36.82 81 11.17 34.18 33.89 239 32.97

109 15.03 45.61 43.25 70 9.66 28.11 27.78 73 10.07 30.80 28.97 252 34.76

60 8.28 25.10 25.64 91 12.55 36.55 38.89 83 11.45 35.02 35.47 234 32.28

239 32.97

249 34.34

237 32.69

725 100.00

(b-b) S  95% Lower 95% Upper

7.3418

Pr > S

0.0618

−0.0390 −0.0895 0.0116

Note. a, b, c = first factor classes for 2005–2006 (0506), 2006–2007 (0607) and 2007–2008 (0708) crop seasons, respectively; h, high; m, medium; l, low; S, statistic by test of symmetry; , kappa coefficient; 95% Lower and Upper,  confidence limits.

The manner in which site-specific management is conducted depends on whether or not the spatial distribution of crop parameters is stable over time (Basso et al., 2007; Yamagishi et al., 2003). According to Whelan and McBratney (2000), the concept of sitespecific crop management needs to be validated by testing the null hypothesis of PA, i.e. “given the large temporal variation evident in crop yield relative to the scale of a single field, then the optimal risk aversion strategy is uniform management”. The adoption of differential treatments might hinge on repeatable evidence for the rejection of such hypothesis by researching the value of managing spatial variation in the light of the temporal one. The sensitivity of the spatial distribution from one cropping season to another might be due to the variable climatic conditions, interacting non-linearly with soil and management factors to influence crop growth. However, the statistical modelling of Dang et al. (2011), which related wheat yield to remote sensing data recorded at crop anthesis and to post-anthesis rainfall, could be used to detect the presence of a temporally stable soil constraint.

The most relevant factors influencing crop growth, in a dry environment such as the study site, are mainly the soil physical properties which control water-holding capacity, such as texture, bulk density and organic matter. In fact, Lòpez-Bellido et al. (1996) indicated that a high amount of rainfall in the vegetative period was positively related to yield, due to the clayey texture of Vertisol, the same soil as our study site, that absorbs a large amount of water and retains it for a long period. Therefore, the spatial patterns of durum wheat yield and yield components as well as quality parameters were probably related to changes in the spatial variation of available soil water over the three seasons, as reported in our previous papers (Buttafuoco et al., 2010; Guastaferro et al., 2010). These results confirm the observations of several authors (Basso et al., 2007; Machado et al., 2002; Eghball and Varvel, 1997; McBratney et al., 1997), regarding the important role of climatic conditions in altering the spatial distribution of rainfed crop response. In general, water deficit during the wheat growth period and around anthesis causes yield losses due to reduction in potential grain number per

M. Diacono et al. / Field Crops Research 131 (2012) 49–62

unit land area (van Herwaarden et al., 1998; Albrizio et al., 2010). On the other hand, drought stresses and high temperatures during grain filling, such as in 0607, can reduce mean kernel weight by decreasing daily rates of translocation of carbohydrate reserves from vegetative organs to the grain (Plaut et al., 2004). Our previous studies (Castrignanò et al., 2007; Guastaferro et al., 2010) have also revealed that the southern part of the field was relatively fine textured, and had a higher content of organic matter. Consequently, during the relatively wet growing season (0506) this area had more optimal water conditions and, hence, greater crop productivity. The difference seen in the next two (relatively dry) seasons is difficult to explain, but might have been caused by other abiotic (i.e. monthly temperatures) or biotic (i.e. soil microorganisms) factors (Fridgen et al., 2004). The observed changes in spatial yield patterns over the three seasons illustrate the substantial influence of meteorological patterns, especially under rainfed conditions. Moreover, there might be a negative effect of the continuous cropping system on soil fertility. In dry areas, the monoculture uses water and N less efficiently than when wheat is grown in rotations with other plants. The N nutrition is largely considered as the decisive factor both in producing high yields and affecting the quality of the grain, because it increases protein concentration (Garrido-Lestache et al., 2005). Our results showed that the grain quality was lower in the areas where more semolina could be produced (as shown by the highest values of test weight) in the 0607 and 0708 seasons. These results confirmed the known inverse relation between wheat quality parameters and grain yield, also reported by Montemurro et al. (2008). Research on the responses of winter wheat in continuous cultivation and in rotation showed that the introduction of different crops in the cultivation system may increase productivity and sustainability of agriculture (Montemurro et al., 2008). The present study allowed the delineation of relatively uniform classes, which could be submitted to site-specific management only after rejecting the null hypothesis (Whelan and McBratney, 2000). Precision Agriculture, coupled with other sustainable management practices such as crop rotation, might be the key to a successful durum wheat cultivation in Mediterranean environment (Basso et al., 2011). Based on the above considerations, it might also be a good agronomic practice to try to improve the soil organic matter content of the northern part of this field, to improve water retention.

5. Conclusions The rainfed nature of durum wheat cropping in many Mediterranean countries makes it sensitive to climatic variation from year to year. Therefore, farmers are recommended to manage crops differentially to compensate for such variation. Factorial co-Kriging Analysis allowed us to attribute weights to the properties which influenced yield variation, as a function of spatial scale over the seasons. The management classes derived from these analyses were not always quite spatially homogeneous from a productivity point of view, and the more or less productive proportions were variable over the seasons, particularly in the last season. The results of this study have shown that the temporal stability was very low and less than 30% of the homogeneous classes was persistent over time. There is evidence to suggest that the temporal variation was higher than spatial variation in this particular environment. To apply site-specific management in environments like the one above described, there is a need for further research. The generally substantial changes in crop spatial patterns over time

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