Spatial variability of yield and other parameters associated with pear trees

In the last few years, precision agriculture has become commonly used with many crops, particularly cereals, and there is also interest in precision horticulture. Pear is a seasonal fruit and well appreciated by Brazilian people, although it is mostly imported. Brazilian farmers are nowadays trying to increase pear production. Thus, this research aimed at mapping the yield of pear trees in order to study the spatial variability of yield as well as its comparison with spatial variability of soil and plant attributes. The experimental field had 146 pear trees, variety 'Pêra d'água', distributed on a 1.24 ha. Four harvests were performed according to the fruit ripening and from each tree; only the ripe fruits were harvested. In each harvest, all the fruits were weighed and the total yield was obtained based on the sum of each harvest. The soil attributes analyzed were P, K, Ca, Mg, pH in CaCl2, C, Cu, Zn, Fe, Mn and base saturation, and the plant attributes were fruit length, diameter and yield. Yield had low correlation with soil and plant attributes. An index of spatial variability was suggested in this study and helped in classifying levels of spatial dependence of the various soil and plant attributes: very low (fruit length); low (P, fruit diameter), medium (Mg, pH, Cu, Zn, Fe), high (Ca, K, base saturation and yield), and very high (Mn and C).

Establishment of Xaraés and Marandu grasses under levels of soil compaction

In Cerrado soils under grazing, changes occur in physical attributes, such as increased density, decreasing on the size of water stable aggregates, and macroporosity reduction. Thus, the aim of this study was to study the effect of compaction on the establishment of two forages. It was adopted a completely randomized design with three replications, in 2 x 4 factorial design, and two forages (Xaraés grass and Marandu grass), and four levels of compaction (soil densities of 1.0, 1.2, 1.4, and 1.6 Mg m-3). The following variables were evaluated 48 days after sowing: tiller population, plant height, dry matter production of shoots and components, leaf and stem, as well as the root dry mass. The stem dry mass decreased with soil density in a similar manner for both forages. It was observed that the leaf dry mass and shoots dry mass of Xaraés grass remained constant in the levels of soil compaction, not adjusting to any regression model. The establishment of Xaraés grass has not been negatively affected by compaction, which may be suitable for situations where there may be layers that restrict the growth of different forages.

Management zones in agriculture acording to the soil and landscape variables

The study of spatial variability of soil and plants attributes, or precision agriculture, a technique that aims the rational use of natural resources, is expanding commercially in Brazil. Nevertheless, there is a lack of mathematical analysis that supports the correlation of these independent variables and their interactions with the productivity, identifying scientific standards technologically applicable. The aim of this study was to identify patterns of soil variability according to the eleven physical and seven chemical indicators in an agricultural area. It was used two multivariate techniques: the hierarchical cluster analysis (HCA) and the principal component analysis (PCA). According to the HCA, the area was divided into five management zones: zone 1 with 2.87ha, zone 2 with 0.8ha, zone 3 with 1.84ha, zone 4 with 1.33ha and zone 5 with 2.76ha. By the PCA, it was identified the most important variables within each zone: V% for the zone 1, CTC in the zone 2, levels of H+Al in the zone 4 and sand content and altitude in the zone 5. The zone 3 was classified as an intermediate zone with characteristics of all others. According to the results it is concluded that it is possible to separate into groups (management zones) samples with the same patterns of variability by the multivariate statistical techniques.

Modalities for soil preparation and gypsum application in ultisol: stem productivity of sugarcane

The study was conducted in an area of expansion of sugarcane at Vale do Paraná factory in Suzanápolis city - São Paulo (SP), in Brazil, in the northwestern region of the State of São Paulo. It was used the sugarcane variety RB92-5345, 1.5m of spacing between rows, in an Ultisol. The study aimed to evaluate the productivity of sugarcane and first ratoon and some soil chemical attributes in function of soil tillage and application or not of gypsum. The experimental design was randomized blocks with six treatments, in a factorial 3x2 and six replicates, the main treatments were soil tillage with three equipments, moldboard plow, chisel plow, and heavy harrow, and two secondary treatments with application of 1 t ha-1of gypsum and no gypsum. After each harvest of cane, the soil was characterized as to its fertility indicators in layers of 0.0-0.15; 0.15-0.30 and 0.30-0.45m. Differences in values of soil chemical attributes due to the methods of preparation occurred in the sugarcane did not last until the harvest of the 1st ratoon cane, and also did not influence the crop productivity. The gypsum application resulted in higher values ​​of total recoverable sugar (TRS) and the productivity of tons of stems per hectare (TSH) to sugarcane and 1st ratoon cane, respectively, confirming the initial hypothesis.

Vocalization data mining for estimating swine stress conditions

This study aimed to identify differences in swine vocalization pattern according to animal gender and different stress conditions. A total of 150 barrow males and 150 females (Dalland® genetic strain), aged 100 days, were used in the experiment. Pigs were exposed to different stressful situations: thirst (no access to water), hunger (no access to food), and thermal stress (THI exceeding 74). For the control treatment, animals were kept under a comfort situation (animals with full access to food and water, with environmental THI lower than 70). Acoustic signals were recorded every 30 minutes, totaling six samples for each stress situation. Afterwards, the audios were analyzed by Praat® 5.1.19 software, generating a sound spectrum. For determination of stress conditions, data were processed by WEKA® 3.5 software, using the decision tree algorithm C4.5, known as J48 in the software environment, considering cross-validation with samples of 10% (10-fold cross-validation). According to the Decision Tree, the acoustic most important attribute for the classification of stress conditions was sound Intensity (root node). It was not possible to identify, using the tested attributes, the animal gender by vocal register. A decision tree was generated for recognition of situations of swine hunger, thirst, and heat stress from records of sound intensity, Pitch frequency, and Formant 1.

Outlining precision boundaries among areas with different variability standards using magnetic susceptibility and geomorphic surfaces

There is an increasing demand for detailed maps that represent in a simplified way the knowledge of the variability of a particular area or region maps. The objective was to outline precision boundaries among areas with different accuracy variability standards using magnetic susceptibility and geomorphic surfaces. The study was conducted in an area of 110 ha, which identified three compartment landscapes based on the geomorphic surfaces model. To determinate pH, organic matter, phosphorus, potassium and magnesium, the total sand and clay, 514 soil samples were collected at depths of 0-0.20 m and 0.60-0.80 m. The sum of base, cationic exchange capacity and base saturation were calculated and the magnetic susceptibility was evaluated in the laboratory using a system based on a balance of analytical precision method. Geomorphic surfaces identification allowed setting specific management areas (locations with maximum homogeneity of soil attributes). The map of spatial variability of magnetic susceptibility can be used to validate the precise boundaries among geomorphic surfaces identified in the field and infer the variability of clay content and soil base saturation.

Vocalization of broilers can be used to identify their sex and genetic strain

In order to reach higher broiler performance, farmers target losses reduction. One way to make this possible is by rearing sexed broilers as male and female present diverse performance due to their physiological differences. Birds from different genetic strain also have a distinct performance at the same age. Considering that sexed flocks may present higher performance this study aimed to identify one-day-old chicks’ sex throughout their vocalization. This research also investigated the possibility of identifying the genetic strain by their vocalization attributes. A total of 120 chicks, half of them were from Cobb® genetic strain and the other half from Ross® genetic strain. From each group, a total of 30 were males and 30 females, which were previously separated by sex using their secondary physiological characteristics at the hatchery. Vocalizations audio recording was done inside a semi-anechoic chamber using a unidirectional microphone connected to an audio input of a digital recorder. Vocalizations were recorded for two minutes. Acoustic characteristics of the sounds were analyzed being calculated the fundamental frequency Pitch, the sound intensity, the first formant, and second formant. Results indicated that the vocalizations of both sexes could be identified by the second formant, and the genetic strain was detected by both the second formant and the Pitch.

Management zones using fuzzy clustering based on spatial-temporal variability of soil and corn yield

Clustering soil and crop data can be used as a basis for the definition of management zones because the data are grouped into clusters based on the similar interaction of these variables. Therefore, the objective of this study was to identify management zones using fuzzy c-means clustering analysis based on the spatial and temporal variability of soil attributes and corn yield. The study site (18 by 250-m in size) was located in Jaboticabal, São Paulo/Brazil. Corn yield was measured in one hundred 4.5 by 10-m cells along four parallel transects (25 observations per transect) over five growing seasons between 2001 and 2010. Soil chemical and physical attributes were measured. SAS procedure MIXED was used to identify which variable(s) most influenced the spatial variability of corn yield over the five study years. Basis saturation (BS) was the variable that better related to corn yield, thus, semivariograms models were fitted for BS and corn yield and then, data values were krigged. Management Zone Analyst software was used to carry out the fuzzy c-means clustering algorithm. The optimum number of management zones can change over time, as well as the degree of agreement between the BS and corn yield management zone maps. Thus, it is very important take into account the temporal variability of crop yield and soil attributes to delineate management zones accurately.