Efeito de subdoses de 2,4-D na produtividade de uva itália e suscetibilidade da cultura em função de seu estádio de desenvolvimento

Durante os anos agrícolas de 2002-2003 e 2003-2004 foram conduzidos trabalhos no município de Maringá - PR, com o objetivo de avaliar o dano potencial de subdoses de 2,4-D sobre plantas de uva, imitando depósitos decorrentes de deriva. No primeiro experimento, a aplicação foi realizada cerca de 30 dias após a poda de inverno, num pomar de uva Itália. As doses utilizadas foram de 6,72; 13,44; 26,88; 53,76 e 107,52 g de equivalente ácido (e.a.) por hectare de 2,4-D, equivalentes a depósitos de 1,0%; 2,0%; 4,0%; 8,0% e 16,0%, assumindo-se uma aplicação de 1 L ha-1 (670 g e.a. ha-1). Nessa data, as plantas encontravam-se na fase de emissão de cachos e florescimento (estádio 15). O surgimento de sintomas visuais de fitointoxicação foi imediato e proporcional às doses aplicadas. A produtividade da cultura foi afetada por todas as doses aplicadas nesse estádio de crescimento. No entanto, mesmo com as injúrias severas registradas na dose mais alta, as plantas afetadas se recuperaram após duas podas para as condições de manejo regionais (duas safras por ano). No segundo experimento, foram aplicadas doses equivalentes a derivas de 1,0 e 2,0% (6,72 e 13,44 g e.a. ha-1) em três estádios do ciclo de desenvolvimento. A aplicação de doses < 13,44 g e.a. ha-1 (2,0% de deriva simulada) a partir do estádio de "meia-baga", não causou repercussões negativas em termos de injúrias visuais e produtividade.

Mapping of summer crops in the State of Paraná, Brazil, through the 10-day spot vegetation NDVI composites

The search for low subjectivity area estimates has increased the use of remote sensing for agricultural monitoring and crop yield prediction, leading to more flexibility in data acquisition and lower costs comparing to traditional methods such as census and surveys. Low spatial resolution satellite images with higher frequency in image acquisition have shown to be adequate for cropland mapping and monitoring in large areas. The main goal of this study was to map the Summer crops in the State of Paraná, Brazil, using 10-day composition of NDVI SPOT Vegetation data for 2005/2006, 2006/2007 and 2007/2008 cropping seasons. For this, a supervised digital classification method with Parallelepiped algorithm in multitemporal RGB image composites was used, in order to generate masks of Summer cultures for each 10-day composition. Accuracy assessment was performed using Kappa index, overall accuracy and Willmott's concordance index, resulting in good levels of accuracy. This methodology allowed the accomplishment, with free and low resolution data, of the mapping of Summer cultures at State level.

Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.

Using data mining to identify factors that influence the degree of leg injuries in broilers

Locomotor problems prevent the bird to move freely, jeopardizing the welfare and productivity, besides generating injuries on the legs of chickens. The objective of this study was to evaluate the influence of age, use of vitamin D, the asymmetry of limbs and gait score, the degree of leg injuries in broilers, using data mining. The analysis was performed on a data set obtained from a field experiment in which it was used two groups of birds with 30 birds each, a control group and one treated with vitamin D. It was evaluated the gait score, the asymmetry between the right and left toes, and the degree of leg injuries. The Weka ® software was used in data mining. In particular, C4.5 algorithm (also known as J48 in Weka environment) was used for the generation of a decision tree. The results showed that age is the factor that most influences the degree of leg injuries and that the data from assessments of gait score were not reliable to estimate leg weakness in broilers.

The use of ALOS/PALSAR data for estimating sugarcane productivity

Some models have been developed using agrometeorological and remote sensing data to estimate agriculture production. However, it is expected that the use of SAR images can improve their performance. The main objective of this study was to estimate the sugarcane production using a multiple linear regression model which considers agronomic data and ALOS/PALSAR images obtained from 2007/08, 2008/09 and 2009/10 cropping seasons. The performance of models was evaluated by coefficient of determination, t-test, Willmott agreement index (d), random error and standard error. The model was able to explain 79%, 12% and 74% of the variation in the observed productions of the 2007/08, 2008/09 and 2009/10 cropping seasons, respectively. Performance of the model for the 2008/09 cropping season was poor because of the occurrence of a long period of drought in that season. When the three seasons were considered all together, the model explained 66% of the variation. Results showed that SAR-based yield prediction models can contribute and assist sugar mill technicians to improve such estimates.