Forecasting the spatiotemporal variability of soil CO2 emissions in sugarcane areas in southeastern Brazil using artificial neural networks.

Carbon dioxide (CO2) is considered one of the main greenhouse effect gases and contributes significantly to global climate change. In Brazil, the agricultural areas offer an opportunity to mitigate this effect, especially with the sugarcane crop, since, depending on the management system, sugarcane stores large amounts of carbon, thereby removing it from the atmosphere. The CO2 production in soil and its transport to the atmosphere are the results of biochemical processes such as the decomposition of organic matter and roots and the respiration of soil organisms, a phenomenon called soil CO2 emissions (FCO2). The objective of the study was to investigate the use of neural networks with backpropagation algorithm to predict the spatial patterns of soil CO2 emission during short periods in sugarcane areas. FCO2 values were collected in three commercial crop areas in the São Paulo state, southeastern Brazil, registered through the LI-8100 system during the years 2008 (Motuca), 2010 (Guariba city), and 2012 (Pradópolis), in the period after the mechanical harvesting (green cane). A neural network multilayer perceptron with a backpropagation algorithm was applied to estimate the FCO2 in 2012, using data from 2008 and 2010 as training for the neural network. The neural network initially presented a mean absolute percentage error (MAPE) of 18.3852 and a coefficient of determination (R2) of 0.9188. Data obtained from the observed and estimated values of FCO2 present moderate spatial dependence, and it is observed from the maps of the spatial pattern of the CO2 flow that the results from the neural network show considerable similarity to the observed data. The model results identify the higher and lower characteristics in sample points of CO2 emissions and produce an overestimation of the range of spatial dependence (0.45 m) and an underestimation of the interpolated values in the field (R2 = 0.80; MAPE = 12.0591), when compared to the actual soil CO2 emission values. Therefore, the results indicate that the artificial neural network provides reliable estimates for the evaluation of FCO2 from data of the soil's physical and chemical attributes and describes the spatial variability of FCO2 in sugarcane fields, thereby contributing to the reduction of uncertainties associated with FCO2 accountings in these areas.

Lack of effects of glyphosate and glufosinate on growth, mineral content, and yield of glyphosate- and glufosinate-resistant maize.

Whether herbicides used in transgenic, herbicide-resistant crops have negative effects on those crops has been controversial. Most all of the data on this topic has been on glyphosate-resistant (GR) soybean, with little information available on GR and glufosinate-resistant (GluR) maize. A GR plus GluR maize variety was evaluated in the greenhouse and the field for effects of glyphosate and glufosinate on growth, mineral content, and yield. Treatments were: 1) a herbicide-free control; 2) 980 g acid equivalent (a.e.) ha-1 glyphosate at 21 days after emergence (DAE); 3) 600 g active ingredient (a.i.) ha-1 glufosinate at 21 DAE; 4) sequential applications of glyphosate at 520 and 980 g a.e. ha-1 at 14 and 28 DAE, respectively; 5) sequential applications of glufosinate at 300 and 300 g a.i. ha-1 at 14 and 28 DAE, respectively; and 6) sequential application of glyphosate (980 g a.e. ha-1) and glufosinate (600 g a.i. ha-1) at 14 and 28 DAE, respectively. None of the herbicide treatments affected plant growth, yield, or content of N, P, K, Ca, Mg, S, Mn, Fe, Cu, or Zn in the greenhouse or field. In grain of field-grown plants, no glufosinate was found and glyphosate (0.12 ng g-1) was only found in the sequential glyphosate treatment.

Impact of the timing and duration of weed control on the establishment of a rubber tree plantation.

Rubber tree production is reduced by weeds that compete for environmental resources; therefore, the timing and duration of weed control influences weed interference. The objectives of this study were to evaluate the growth of rubber tree (Hevea brasiliensis) plants, to determine the critical period for weed control, and to evaluate the growth recovery of rubber trees that coexisted with weeds for different periods of time after planting. Two groups of treatments were established under field conditions in the first year of the investigation: one group contained crescent periods of weed infestation, while the other contained crescent periods of weed control, also including a weed-free check and a total weedy check. In the second year of the investigation, the weeds were totally controlled. Urochloa decumbens was the dominant weed (over 90% groundcover). Crop growth was greatly reduced due to the weed interference. Plant height decreased more rapidly than did any other characteristic. Plant height, leaf dry mass, and leaf area decreased by 99%, 97% and 96%, respectively, and were the most reduced characteristics. Plant height also recovered more rapidly than did any characteristic when the period of weed control was lengthened. However, stem dry mass increased by 750%, making it the most recovered characteristic. The critical period for weed control was between 4 and 9½ months after planting in the first year; however, the rubber trees showed an expressive growth recovery when the weeds were controlled throughout the second year.

Hormesis with glyphosate depends on coffee growth stage.

Weed management systems in almost all Brazilian coffee plantations allow herbicide spray to drift on crop plants. In order to evaluate if there is any effect of the most commonly used herbicide in coffee production, glyphosate, on coffee plants, a range of glyphosate doses were applied directly on coffee plants at two distinct plant growth stages. Although growth of both young and old plants was reduced at higher glyphosate doses, low doses caused no effects on growth characteristics of young plants and stimulated growth of older plants. Therefore, hormesis with glyphosate is dependent on coffee plant growth stage at the time of herbicide application.