Maize and soybeans production in integrated system under no-tillage with different pasture combinations and animal categories

The adoption of no-till system (NTS) combined with crop-livestock integration (CLI) has been a strategy promoted in Brazil, aiming to maximize areas yield and increase agribusiness profitability. This study aimed to evaluate grains yield and phytotechnical attributes from maize and soybean culture by CLI system under NTS after winter annual pure and diversified pastures with the absence or presence of grazing animals. The experiment was installed in Castro (Parana State, Brazil) on in a dystrophic Humic Rhodic Hapludox with a clay texture, using experimental design of randomized complete blocks in 4 x 2 factorial scheme with three replications. Treatments included four pasture combinations (diversified or pure) and animal categories (light and heavy) subjected or not to grazing animals during the winter. During 2008/09 and 2009/10 summers, the area was cultivated with soybeans and maize, respectively, with yield assessment of grains and phytotechnical attributes. Treatments did not alter the yield and weight of a thousand seeds (WTS) of soybeans. In maize culture, the grazing animal during the winter increased the plant population and grains yield, but gave slight decrease in WTS. Pasture combinations (diversified or pure) and animal categories (light and heavy) did not interfere in soybean culture, but benefited the maize crop.

DISTRIBUTION OF NITROGEN AMMONIUM SULFATE (N-15) SOIL-PLANT SYSTEM IN A NO-TILLAGE CROP SUCCESSION

DISTRIBUTION OF NITROGEN AMMONIUM SULFATE (N-15) SOIL-PLANT SYSTEM IN A NO-TILLAGE CROP SUCCESSION The N use by maize (Zea mays, L.) is affected by N-fertilizer levels. This study was conducted using a sandy-clay texture soil (Hapludox) to evaluate the efficiency of N use by maize in a crop succession, based on N-15-labeled ammonium sulfate (5.5 atom %) at different rates, and to assess the residual fertilizer effect in two no-tillage succession crops (signalgrass and corn). Two maize crops were evaluated, the first in the growing season 2006, the second in 2007, and brachiaria in the second growing season. The treatments consisted of N rates of 60, 120 and 180 kg ha(-1) in the form of labeled N-15 ammonium sulfate. This fertilizer was applied in previously defined subplots, only to the first maize crop (growing season 2006). The variables total accumulated N; fertilizer-derived N in corn plants and pasture; fertilizer-derived N in the soil; and recovery of fertilizer-N by plants and soil were evaluated. The highest uptake of fertilizer N by corn was observed after application of 120 kg ha(-1) N and the residual effect of N fertilizer on subsequent corn and Brachiaria was highest after application of 180 kg ha(-1) N. After the crop succession, soil N recovery was 32, 23 and 27 % for the respective applications of 60, 120 and 180 kg ha(-1) N.

Dissecting Phaseolus vulgaris Innate Immune System against Colletotrichum lindemuthianum Infection

Background: The genus Colletotrichum is one of the most economically important plant pathogens, causing anthracnose on a wide range of crops including common beans (Phaseolus vulgaris L.). Crop yield can be dramatically decreased depending on the plant cultivar used and the environmental conditions. This study aimed to identify potential genetic components of the bean immune system to provide environmentally friendly control measures against this fungus. Methodology and Principal Findings: As the common bean is not amenable to reverse genetics to explore functionality and its genome is not fully curated, we used putative Arabidopsis orthologs of bean expressed sequence tag (EST) to perform bioinformatic analysis and experimental validation of gene expression to identify common bean genes regulated during the incompatible interaction with C. lindemuthianum. Similar to model pathosystems, Gene Ontology (GO) analysis indicated that hormone biosynthesis and signaling in common beans seem to be modulated by fungus infection. For instance, cytokinin and ethylene responses were up-regulated and jasmonic acid, gibberellin, and abscisic acid responses were down-regulated, indicating that these hormones may play a central role in this pathosystem. Importantly, we have identified putative bean gene orthologs of Arabidopsis genes involved in the plant immune system. Based on experimental validation of gene expression, we propose that hypersensitive reaction as part of effector-triggered immunity may operate, at least in part, by down-regulating genes, such as FLS2-like and MKK5-like, putative orthologs of the Arabidopsis genes involved in pathogen perception and downstream signaling. Conclusions/Significance: We have identified specific bean genes and uncovered metabolic processes and pathways that may be involved in the immune response against pathogens. Our transcriptome database is a rich resource for mining novel defense-related genes, which enabled us to develop a model of the molecular components of the bean innate immune system regulated upon pathogen attack.

Active crop sensor to detect variability of nitrogen supply and biomass on sugarcane fields

Nitrogen management has been intensively studied on several crops and recently associated with variable rate on-the-go application based on crop sensors. Such studies are scarce for sugarcane and as a biofuel crop the energy input matters, seeking high positive energy balance production and low carbon emission on the whole production system. This article presents the procedure and shows the first results obtained using a nitrogen and biomass sensor (N-Sensor (TM) ALS, Yara International ASA) to indicate the nitrogen application demands of commercial sugarcane fields. Eight commercial fields from one sugar mill in the state of Sao Paulo, Brazil, varying from 15 to 25 ha in size, were monitored. Conditions varied from sandy to heavy soils and the previous harvesting occurred in May and October 2009, including first, second, and third ratoon stages. Each field was scanned with the sensor three times during the season (at 0.2, 0.4, and 0.6 m stem height), followed by tissue sampling for biomass and nitrogen uptake at ten spots inside the area, guided by the different values shown by the sensor. The results showed a high correlation between sensor values and sugarcane biomass and nitrogen uptake, thereby supporting the potential use of this technology to develop algorithms to manage variable rate application of nitrogen for sugarcane.

Analysis of NDVI time series using cross-correlation and forecasting methods for monitoring sugarcane fields in Brazil

Brazil is the largest sugarcane producer in the world and has a privileged position to attend to national and international market places. To maintain the high production of sugarcane, it is fundamental to improve the forecasting models of crop seasons through the use of alternative technologies, such as remote sensing. Thus, the main purpose of this article is to assess the results of two different statistical forecasting methods applied to an agroclimatic index (the water requirement satisfaction index; WRSI) and the sugarcane spectral response (normalized difference vegetation index; NDVI) registered on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite images. We also evaluated the cross-correlation between these two indexes. According to the results obtained, there are meaningful correlations between NDVI and WRSI with time lags. Additionally, the adjusted model for NDVI presented more accurate results than the forecasting models for WRSI. Finally, the analyses indicate that NDVI is more predictable due to its seasonality and the WRSI values are more variable making it difficult to forecast.

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, Sa approximate to o Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3.3 L/s and 2.8 L/s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of 3.1 L/s and 3.0 L/s for training and test set respectively, which represented about 12% of average consumption. DOI: 10.1061/(ASCE)WR.1943-5452.0000177. (C) 2012 American Society of Civil Engineers.

Nitrogen fertilizer (15N) leaching in a central pivot fertigated coffee crop

Nitrogen has a complex dynamics in the soil-plant-atmosphere system. N fertilizers are subject to chemical and microbial transformations in soils that can result in significant losses. Considering the cost of fertilizers, the adoption of good management practices like fertigation could improve the N use efficiency by crops. Water balances (WB) were applied to evaluate fertilizer N leaching using 15N labeled urea in west Bahia, Brazil. Three scenarios (2008/2009) were established: i) rainfall + irrigation the full year, ii) rainfall only; and iii) rainfall + irrigation only in the dry season. The water excess was considered equal to the deep drainage for the very flat area (runoff = 0) with a water table located several meters below soil surface (capillary rise = 0). The control volume for water balance calculations was the 0 - 1 m soil layer, considering that it involves the active root system. The water drained below 1 m was used to estimate fertilizer N leaching losses. WB calculations used the mathematic model of Penman-Monteith for evapotranspiration, considering the crop coefficient equal to unity. The high N application rate associated to the high rainfall plus irrigation was found to be the main cause for leaching, which values were 14.7 and 104.5 kg ha-1 for the rates 400 and 800 kg ha-1 of N, corresponding to 3.7 and 13.1 % of the applied fertilizer, respectively.