Modeling and identification of fertility maps using artificial neural networks

The application of agricultural fertilizers using variable rates along the field can be made through fertility maps previously elaborated or through real-time sensors. In most of the cases applies maps previously elaborated. These maps are identified from analyzes done in soil samples collected regularly (a sample for each field cell) or irregularly along the field. At the moment, mathematical interpolation methods such as nearest neighbor, local average, weighted inverse distance, contouring and kriging are used for predicting the variables involved with elaboration of fertility maps. However, some of these methods present deficiencies that can generate different fertility maps for a same data set. Moreover, such methods can generate inprecise maps to be used in precision farming. In this paper, artificial neural networks have been applied for elaboration and identification of precise fertility maps which can reduce the production costs and environmental impacts.

Variabilidade espacial de atributos químicos do solo em Araguari-MG

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Mapeamento da fertilidade do solo utilizando sistema de informação geográfica

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Refined genome-wide comparative map of the domestic horse, donkey and human based on cross-species chromosome painting: insight into the occasional fertility of mules

We have made a complete set of painting probes for the domestic horse by degenerate oligonucleotide-primed PCR amplification of flow-sorted horse chromosomes. The horse probes, together with a full set of those available for human, were hybridized onto metaphase chromosomes of human, horse and mule. Based on the hybridization results, we have generated genome-wide comparative chromosome maps involving the domestic horse, donkey and human. These maps define the overall distribution and boundaries of evolutionarily conserved chromosomal segments in the three genomes. Our results shed further light on the karyotypic relationships among these species and, in particular, the chromosomal rearrangements that underlie hybrid sterility and the occasional fertility of mules.

Spatial distribution of Yellow Sigatoka Leaf Spot correlated with soil fertility and plant nutrition

This study analyzed the spatial distribution of Yellow Sigatoka Leaf Spot relative to soil fertility and plant nutritional status using geostatistics. The experimental area comprised 1.2 ha, where 27 points were georeferenced and spaced on a regular grid 18 × 18 m. The severity of Yellow Sigatoka, soil fertility and plant nutritional status were evaluated at each point. The spherical model was adjusted for all variables using restricted maximum likelihood. Kriging maps showed the highest infection rate of Sigatoka occurred in high areas of the field which had the highest concentration of sand, while the lowest disease was found in lower areas with lower silt, organic matter, total exchangeable bases, effective cation exchange capacity, base saturation, Ca and Mg in soil, and foliar sulfur (S). These results may help farmers manage Yellow Sigatoka disease more effectively, with balanced fertilization and reduced fungicide application. This practice minimizes the environmental impact and cost of production while contributing to production sustainability.

Yield mapping, soil fertility and tree gaps in an orange orchard

The current high competition on Citrus industry demands from growers new management technologies for superior efficiency and sustainability. In this context, precision agriculture (PA) has developed techniques based on yield mapping and management systems that recognize field spatial variability, which contribute to increase profitability of commercial crops. Because spatial variability is often not perceived the orange orchards are still managed as uniform and adoption of PA technology on citrus farms is low. Thus, the objective of the present study was to characterize the spatial variability of three factors: fruit yield, soil fertility and occurrence of plant gaps caused by either citrus blight or huanglongbing (HLB) in a commercial Valencia orchard in Brotas, São Paulo State, Brazil. Data from volume, geographic coordinates and representative area of the bags used on harvest were recorded to generate yield points that were then interpolated to produce the yield map. Soil chemical characteristics were studied by analyzing samples collected along planting rows and inter-rows in 24 points distributed in the field. A map of density of tree gaps was produced by georeferencing individual gaps and later by counting the number of gaps within 500 m² cells. Data were submitted to statistical and geostatistical analyses. A t test was used to compare means of soil chemical characteristics between sampling regions. High variation on yield and density of tree gaps was observed from the maps. It was also demonstrated overlapping regions of high density of plant absence and low fruit yield. Soil fertility varied depending on the sampling region in the orchard. The spatial variability found on yield, soil fertility and on disease occurrence demonstrated the importance to adopt site specific nutrient management and disease control as tools to guarantee efficiency of fruit production.

Spatial variability of soil fertility and its relationship with seed physiological potential in a soybean production area

The correlation of soil fertility x seed physiological potential is very important in the area of seed technology but results published with that theme are contradictory. For this reason, this study to evaluate the correlations between soil chemical properties and physiological potential of soybean seeds. On georeferenced points, both soil and seeds were sampled for analysis of soil fertility and seed physiological potential. Data were assessed by the following analyses: descriptive statistics; Pearson's linear correlation; and geostatistics. The adjusted parameters of the semivariograms were used to produce maps of spatial distribution for each variable. Organic matter content, Mn and Cu showed significant effects on seed germination. Most variables studied presented moderate to high spatial dependence. Germination and accelerated aging of seeds, and P, Ca, Mg, Mn, Cu and Zn showed a better fit to spherical semivariogram: organic matter, pH and K had a better fit to Gaussian model; and V% and Fe showed a better fit to the linear model. The values for range of spatial dependence varied from 89.9 m for P until 651.4 m for Fe. These values should be considered when new samples are collected for assessing soil fertility in this production area.

Genetic maps of the proximal half of chromosome arm 2L of Drosophila melanogaster

Drosophila mutants have played an important role in elucidating the physiologic function of genes. Large-scale projects have succeeded in producing mutations in a large proportion of Drosophila genes. Many mutant fly lines have also been produced through the efforts of individual laboratories over the past century. In an effort to make some of these mutants more useful to the research community, we systematically mapped a large number of mutations affecting genes in the proximal half of chromosome arm 2L to more precisely defined regions, defined by deficiency intervals, and, when possible, by individual complementation groups. To further analyze regions 36 and 39-40, we produced 11 new deficiencies with gamma irradiation, and we constructed 6 new deficiencies in region 30-33, using the DrosDel system. trans-heterozygous combinations of deficiencies revealed 5 additional functions, essential for viability or fertility.