1

Proceeding of the 10th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences

Florianopolis-SC, Brazil, July 10-13, 2012.

Spatial variability of the common bean Pratylenchusbrachyurus

R. Noetzold1,2, M.C. Alves1,3, D. Cassetari-Neto1, A.P. Pires1,4

1 Federal University of MatoGrosso, Cuiabá - MT

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2 Capes support

3 CNPqsupport

4 CNPqsupport

Abstract

The objective was to evaluate the spatial distribution of Pratylenchusbrachyurus in common bean and to determine the structure of spatial dependence of the nematode in the field. Soil samples were collected two days before the harvest of common bean irrigated at a depth of 0-0.2 m in 59 georeferencedpoints. Subsequently, the samples were sent to the laboratory of plant pathology. The extraction was performed by flotation and centrifugation in sucrose solution, followed by counting and identification of nematodes. By having the data, we performed geostatistical analyzes whit the software R, package geoR. The adjustment was made usingspherical, exponential and gaussianvariograms models. The Akaike information criterion was used to select the best model fit. There was an average, maximum and minimum value of 618, 1880 and 10 P. brachyurus total (soil and roots), respectively. The largest concentration of plant parasitic nematode occurred in the boundary of the crop with foci located in the northwest and south. The best fit of the spherical model presented range of 203.62 m. It was possible to detect the spatial dependence of P.brachyurus. Simple krigingerrors were found mainly in the southwestern region, with greater disability in the sample.

Keywords: Akaikecriterion, spherical model, simple kriging.

  1. Introduction

Common bean is considered the main source of protein in the diet of the population, constituting together with the rice, the food base of the Brazilian population. The species belongs to the legume family, with cycle of 90 to 100 days and a shallow root system (Inforzatoet al., 1964). Among the winter crops irrigated by sprinkling, the common bean is the main in crop the southeast, midwest and northeast parts of Brazil. The cropping systems in summer, called "winter", common bean, which seeding occurs from May to June is more technified than the others, using, in addition to irrigation, other inputs such as good quality seeds, fertilizers, lime and pesticides, allowing theobtaintion of products three to five times higher than the others(Stone and Pereira,1994).

The area planted to with common bean and sprinkler irrigation, in states that use this technology, is about 160.000 ha, with average yield 1370-1724 kg ha-1 of grain. In the cultivation of irrigated beans, called the winter season (May-June), the farmer is encouraged to use higher levels of technology, achieving higher productivity at other times of crop cultivation. Other advantages of the irrigated crop are: possibility of producing high quality seeds, supply of the product during the season, under the best price and ease of workmanship (Stone andMoreira, 1986).

As in other annual crops, common bean inspires care health issue of agriculture. There is a great lack of information on pathogenicity, damage and reproduction of species of nematodes on common bean (Goulart, 2008). In the nematology laboratory of EmbrapaCerrado, large populations of Pratylenchusbrachyurus have been found in samples of soil and roots of beans from around the Brazil Midwest. There are reports of loss of great concern, and likely occurrence of other pathogens associated with soil compression. Despite these reports, the damage caused by Pratylenchus spp. in various farming systems have not been well characterized, especially in the Cerrado, making its control a challenge to the productive sector (Goulart, 2008). In Brazil,according toFerraz(1999), the bean crop is among the leading hosting the genus Pratylenchus, but is hosted by soybeans, oats, corn, millet, sunflower, sugar cane, cotton, some green manures and most weeds, making it difficult to control through crop rotation (Dias et al., 2007).

Species of Pratylenchusare migratory endoparasites. The penetration and move-tion of nematodes in the roots cause the destruction of the root system, impairing the absorption of nutrients, damaging the roots and injuries, serving as a gateway to other pathogens.

The increase in the spread of Pratylenchus may be related to lack of crop rotation, use of cultivars that are hosts of plant parasitic nematode, no-tillage or minimum tillage that keeps the soil with higher moisture adequate to nematodes, frequent use of soils with sandy, nutritional imbalance, and irrigation use (Goulart, 2008).

The spatial distribution of nematodes in the field has been described as the aggregate type, which implies spatial dependence of data, and basic statistics are often incomplete to describe them in a situation of spatial auto-correlation (Wallace and Hawkins, 1994). The geostatistical methodology is appropriated for analyzing such data, allowing the quantification of spatial dependence between samples, based on the spatial prediction of nematode intensity with minimum variance and without bias (Fariaset al., 2002).

This study aimed to evaluate the spatial distribution of P. brachyurus in plants and determine the structure the spatial dependence of the disease in the common bean crop.

  1. Methodology

Samples were collected in Rotilli Farm, located in Jaciaracity, MT from a commercial farm, located in the common bean crop with pivot irrigation management, two days before the harvest. We collected soil samples and root at a depth of 0-0.2 m, georeferenced in 59 points (Figure 1).

The sample was composed of approximately 500 grams of soil and 5 grams of roots. After the collection, samples were sent to the laboratory of plant pathology, to the extraction of nematodes by the method of centrifugal flotation in sucrose solution (Jenckins, 1964), with subsequent counting of binocular stereoscopic microscope for, identification of the genera of nematodes and eggs.

By having the data, we performed geostatistical analyzes in the Rsoftware,geoRpackage (Diggle and Ribeiro Junior, 2007). The adjustment of the spherical, exponential andgaussianvariograms by Ordinary Least Squares (OLS)method.Simple kriging was performance as interpolation method. Kriging variances were used to evaluation the prediction errors. The Akaike information criterion was used to select the best variogram.

Figure 1: Gridsampling of the experimental area.

3. Preliminary results

Difference was observed of 1870 P. brachyurus sample point between high and low population, thus there was high value for the coefficient of variation. A similar result was found by Dinardo-Miranda and Fracasso (2009), which investigated the spatial variability of nematodes in sugar cane, and found the coefficient of variation between 43.3% to 319.8% in the study areas. The mean and median were similar, the skewness and kurtosis were positively asymmetric distribution and platykurticdistribution, respectively (Table 1).

Table 1:Descriptive statisticsregardingthe populationof P.brachyurusextracted fromsoilandrootsof common bean plants.

Descriptive statistics / P. brachyurus
Minimum / 10.00
Firstquartile / 330.00
Median / 520.00
Mean / 618.00
Three / 760.00
Maximum / 1880.00
Kurtosis / 1.02
Asymmetry / 1.16
Standard deviation / 420.40
Coefficient of variation (%) / 68.06

For data on descriptive statistics, we found that there was variability in the data, but it was not possible to visualize the location of sampling sites with higher and lower population and also did not consider the spatial dependence. In this sense, geostatistical tools were employed to better understand the spatial variability. Through variogram adjustments it was possible to verify that there was spatial dependence of P. brachyurus (Table 2). We observed lower values ​​of AIC with the removal of first-order trend, and the spherical model was chosen for the preparation of simple kriging and its kriging error maps (Figure 2).

The spherical model had the highest range with 380.18 m. After removing the 1 sttendency the spherical model presented the range of 203.62 m (Table 2).

Pinheiroet al. (2008) studied the relationship between soil fertility with the spatial distribution of Heteroderaglycines and observed spherical model adjustment for all variables of the analyzed soybean cyst nematode.

Table 2.Parameters and coefficient of variograms adjusted by the method of ordinary least squares, referring to the total population of P. brachyurusat the root and in the soil.

Model / Nugget Effect / Sill / Range(m) / Practical
Range(m) / AIC
spherical / 28420.00 / 167966.90 / 380.18 / 380.18 / 879.86
spherical* / 14761.99 / 141558.89 / 203.62 / 203.62 / 874.57
exponential / 42927.36 / 166664.40 / 171.20 / 296.31 / 885.39
exponential * / 0.00 / 144797.70 / 85.76 / 256.91 / 875.11
gaussian / 0.00 / 169330.03 / 123.33 / 369.47 / 878.25
gaussian* / 37013.74 / 142267.07 / 108.45 / 187.71 / 876.10

* removal of first-order trend.

Figure 2:Spherical variogram model with first-order trend removal for P. brachyurus.

The largest populations of plant parasitic nematode occurred in the boundary of the crop with the formation of foci located in the regions north and south (Figure 3). Through the gray scale of the simple kriging variance, we found that the greatest errors of kriging were found in the southwestern region, which showed greater impairment in the sample, where the points were away from each other (Figure 3).

Figure3:Simple kriging (left side) and simple kriging variance (right side), referring to the spherical variogram for P. brachyurus.

Studies relating to soil fertility and grain yield in the study area are being conducted to better understand the spatial distribution of P. brachyurus. Research conducted by Franchiniet al. (2011) stated that the spatial variability of chemical attributes is related to the symptoms caused by P. brachyurus.

4. Conclusion

It was possible to detect the spatial dependence of P brachyurus.

The best fit model was the spherical with removal of first order trend.

The largest variance error occurred in the region with the lowest number of sampling points in a southeasterly direction.

Acknowledgments

Rottili farm owners for their cooperation in carrying out this work.

References

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