The impact of El Nino Southern Oscillation phases on off-season maize yield for a subtropical region of Brazil

In recent years, maize has become one of the main alternative crops for the autumn winter growing season in the central-western and southeastern regions of Brazil. However, water deficits, sub-optimal temperatures and low solar radiation levels are common problems that are experienced during this growing season by local farmers. One methodology to assess the impact of variable weather conditions on crop production is the use of crop simulation models. The goal of this study was to evaluate the effect of climate variability on maize yield for a subtropical region of Brazil. Specific objectives for this study were (1) to analyse the effect of El Nino Southern Oscillation (ENSO) on precipitation and air temperature for four locations in the state of Sao Paulo and (2) to analyse the impact of ENSO on maize grown off-season for the same four locations using a crop simulation model. For each site, historical weather data were categorised as belonging to one of three phases of ENSO: El Nino (warm sea surface temperature anomalies in the Pacific), La Nina (cool sea surface temperature anomalies) or neutral, based on an index derived from observed sea surface temperature anomalies. During El Nino, there is a tendency for an increase in the rainfall amount during May for the four selected locations, and also during April, mainly in three of the locations, resulting in an increase in simulated maize yield planted between February 15 and March 15. In general, there was a decrease in the simulated yield for maize grown off-season during neutral years. This study showed how a crop model can be used to assess the impact of climate variability on the yield of maize grown off-season in a subtropical region of Brazil. The outcomes of this study can be very useful for both policy makers and local farmers for agricultural planning and decision making. Copyright (C) 2009 Royal Meteorological Society

WATER RESOURCES SITUATION AT PIRACICABA, CAPIVARI AND JUNDIAI WATERSHEDS USING A DYNAMIC SYSTEMS MODEL

Using a dynamic systems model specifically developed for Piracicaba, Capivari and Jundia River Water Basins (BH-PCJ) as a tool to help to analyze water resources management alternatives for policy makers and decision takers, five simulations for 50 years timeframe were performed. The model estimates water supply and demand, as well as wastewater generation from the consumers at BH-PCJ. A run was performed using mean precipitation value constant, and keeping the actual water supply and demand rates, the business as usual scenario. Under these considerations, it is expected an increment of about similar to 76% on water demand, that similar to 39% of available water volume will come from wastewater reuse, and that waste load increases to similar to 91%. Falkenmark Index will change from 1,403 m(3) person(-1) year(-1) in 2004, to 734 m(3) P(-1) year(-1) by 2054, and the Sustainability Index from 0.44 to 0.20. Another four simulations were performed by affecting the annual precipitation by 90 and 110%; considering an ecological flow equal to 30% of the mean daily flow; and keeping the same rates for all other factors except for ecological flow and household water consumption. All of them showed a tendency to a water crisis in the near future at BH-PCJ.

Estimating hourly net radiation for leaf wetness duration using the Penman-Monteith equation

Leaf wetness duration (LWD) is related to plant disease occurrence and is therefore a key parameter in agrometeorology. As LWD is seldom measured at standard weather stations, it must be estimated in order to ensure the effectiveness of warning systems and the scheduling of chemical disease control. Among the models used to estimate LWD, those that use physical principles of dew formation and dew and/or rain evaporation have shown good portability and sufficiently accurate results for operational use. However, the requirement of net radiation (Rn) is a disadvantage foroperational physical models, since this variable is usually not measured over crops or even at standard weather stations. With the objective of proposing a solution for this problem, this study has evaluated the ability of four models to estimate hourly Rn and their impact on LWD estimates using a Penman-Monteith approach. A field experiment was carried out in Elora, Ontario, Canada, with measurements of LWD, Rn and other meteorological variables over mowed turfgrass for a 58 day period during the growing season of 2003. Four models for estimating hourly Rn based on different combinations of incoming solar radiation (Rg), airtemperature (T), relative humidity (RH), cloud cover (CC) and cloud height (CH), were evaluated. Measured and estimated hourly Rn values were applied in a Penman-Monteith model to estimate LWD. Correlating measured and estimated Rn, we observed that all models performed well in terms of estimating hourly Rn. However, when cloud data were used the models overestimated positive Rn and underestimated negative Rn. When only Rg and T were used to estimate hourly Rn, the model underestimated positive Rn and no tendency was observed for negative Rn. The best performance was obtained with Model I, which presented, in general, the smallest mean absolute error (MAE) and the highest C-index. When measured LWD was compared to the Penman-Monteith LWD, calculated with measured and estimated Rn, few differences were observed. Both precision and accuracy were high, with the slopes of the relationships ranging from 0.96 to 1.02 and R-2 from 0.85 to 0.92, resulting in C-indices between 0.87 and 0.93. The LWD mean absolute errors associated with Rn estimates were between 1.0 and 1.5h, which is sufficient for use in plant disease management schemes.

Bayesian ratemaking procedure of crop insurance contracts with skewed distribution

Over the years, crop insurance programs became the focus of agricultural policy in the USA, Spain, Mexico, and more recently in Brazil. Given the increasing interest in insurance, accurate calculation of the premium rate is of great importance. We address the crop-yield distribution issue and its implications in pricing an insurance contract considering the dynamic structure of the data and incorporating the spatial correlation in the Hierarchical Bayesian framework. Results show that empirical (insurers) rates are higher in low risk areas and lower in high risk areas. Such methodological improvement is primarily important in situations of limited data.

Parametric and nonparametric statistical modelling of crop yield: implications for pricing crop insurance contracts

This article considers alternative methods to calculate the fair premium rate of crop insurance contracts based on county yields. The premium rate was calculated using parametric and nonparametric approaches to estimate the conditional agricultural yield density. These methods were applied to a data set of county yield provided by the Statistical and Geography Brazilian Institute (IBGE), for the period of 1990 through 2002, for soybean, corn and wheat, in the State of Paran. In this article, we propose methodological alternatives to pricing crop insurance contracts resulting in more accurate premium rates in a situation of limited data.

Does using stepwise variable selection to build sequential path analysis models make sense?

Causal inference methods - mainly path analysis and structural equation modeling - offer plant physiologists information about cause-and-effect relationships among plant traits. Recently, an unusual approach to causal inference through stepwise variable selection has been proposed and used in various works on plant physiology. The approach should not be considered correct from a biological point of view. Here, it is explained why stepwise variable selection should not be used for causal inference, and shown what strange conclusions can be drawn based upon the former analysis when one aims to interpret cause-and-effect relationships among plant traits.

Validation of a root water uptake model to estimate transpiration constraints

Experimental results obtained from a greenhouse trial with common bean (Phaseolus vulgaris L) plants performed to test model hypotheses regarding the onset of limiting hydraulic conditions and the shape of the transpiration reduction curve in the falling rate phase are presented. According to these hypotheses based on simulations with an upscaled single-root model, the matric flux potential at the onset of limiting hydraulic conditions is as a function of root length density and potential transpiration rate, while the relative transpiration in the falling rate phase equals the relative matric flux potential. Transpiration of bean plants in water stressed pots with four different soils was determined daily by weighing and compared to values obtained from non-stressed pots. This procedure allowed determining the onset of the falling rate phase and corresponding soil hydraulic conditions. At the onset of the falling rate phase, the value of matric flux potential M(I) showed to differ in order of magnitude from the model predicted value for three out of four soils. This difference between model and experiment can be explained by the heterogeneity of the root distribution which is not considered by the model. An empirical factor to deal with this heterogeneity should be included in the model to improve predictions. Comparing the predictions of relative transpiration in the falling rate phase using a linear shape with water content, pressure head or matric flux potential, the matric flux potential based reduction function, in agreement with the hypothesis, showed the best performance, while the pressure head based equation resulted in the highest deviations between observed and predicted values of relative transpiration rates. (C) 2010 Elsevier B.V. All rights reserved.

Modeling soil organic carbon dynamics in Oxisols of Ibiruba (Brazil) with the Century Model

introduction of conservation practices in degraded agricultural land will generally recuperate soil quality, especially by increasing soil organic matter. This aspect of soil organic C (SOC) dynamics under distinct cropping and management systems can be conveniently analyzed with ecosystem models such as the Century Model. In this study, Century was used to simulate SOC stocks in farm fields of the Ibiruba region of north central Rio Grande do Sul state in Southern Brazil. The region, where soils are predominantly Oxisols, was originally covered with subtropical woodlands and grasslands. SOC dynamics was simulated with a general scenario developed with historical data on soil management and cropping systems beginning with the onset of agriculture in 1900. From 1993 to 2050, two contrasting scenarios based on no-tillage soil management were established: the status quo scenario, with crops and agricultural inputs as currently practiced in the region and the high biomass scenario with increased frequency of corn in the cropping system, resulting in about 80% higher biomass addition to soils. Century simulations were in close agreement with SOC stocks measured in 2005 in the Oxisols with finer texture surface horizon originally under woodlands. However, simulations in the Oxisols with loamy surface horizon under woodlands and in the grassland soils were not as accurate. SOC stock decreased from 44% to 50% in fields originally under woodland and from 20% to 27% in fields under grasslands with the introduction of intensive annual grain crops with intensive tillage and harrowing operations. The adoption of conservation practices in the 1980s led to a stabilization of SOC stocks followed by a partial recovery of native stocks. Simulations to 2050 indicate that maintaining status quo would allow SOC stocks to recover from 81% to 86% of the native stocks under woodland and from 80% to 91 % of the native stocks under grasslands. Adoption of a high biomass scenario would result in stocks from 75% to 95% of the original stocks under woodlands and from 89% to 102% in the grasslands by 2050. These simulations outcomes underline the importance of cropping system yielding higher biomass to further increase SOC content in these Oxisols. This application of the Century Model could reproduce general trends of SOC loss and recovery in the Oxisols of the Ibiruba region. Additional calibration and validation should be conducted before extensive usage of Century as a support tool for soil carbon sequestration projects in this and other regions can be recommended. (C) 2009 Elsevier B.V. All rights reserved.

Contribution of soil organic carbon to the ion exchange capacity of tropical soils

Highly weathered soils represent about 3 billion ha of the tropical region. Oxisols represent about 60% of the Brazilian territory (more than 5 million km 2), in areas of great agricultural importance. Soil organic carbon (SOC) can be responsible for more than 80% of the cation exchange capacity (CEC) of highly weathered soils, such as Oxisols and Ultisols. The objective of this study was to estimate the contribution of the SOC to the CEC of Brazilian soils from different orders. Surface samples (0.0 to 0.2 m) of 30 uncultivated soils (13 Oxisols, 6 Ultisols, 5 Alfisols, 3 Entisols, I Histosol, 1 Inceptisol. and I Molisol), under native forests and from reforestation sites from Sao Paulo State, Brazil, were collected in order to obtain a large variation of (electro)chemical, physical, and mineralogical soil attributes. Total content of SOC was quantified by titulometric and colorimetric methods. Effective cation exchange capacity (ECEC) was obtained by two methods: the indirect method-summation-estimated the ECECi from the sum of basic cations (Ca+ Mg+ K+ Na) and exchangeable Al; and the direct ECECd obtained by the compulsive exchange method, using unbuffered BaCl2 solution. The contribution of SOC to the soil CEC was estimated by the Bennema statistical method. The amount of SOC var ied from 6.6 g kg(-1) to 213.4 g kg(-1). while clay contents varied from 40 g kg(-1) to 716 g kg(-1). Soil organic carbon contents were strongly associated to the clay contents, suggesting that clay content was the primary variable in controling the variability of SOC contents in the samples. Cation exchange capacity varied from 7.0 mmol(c) kg(-1) to 137.8 mmol(c) kg(-1) and had a positive Correlation with SOC. The mean contribution (per grain) of the SOC (1.64 mmol(c)) for the soil CEC was more than 44 times higher than the contribution of the clay fraction (0.04 mmol(c),). A regression model that considered the SOC content as the only significant variable explained 60% of the variation in the soil total CEC. The importance of SOC was related to soil pedogenetic process, since its contribution to the soil CEC was more evident in Oxisols with predominance of Fe and Al (oxihydr)oxides in the mineral fraction or in Ultisols, that presented illuviated clay. The influence of SOC in the sign and in the magnitude of the net charge of soils reinforce the importance of agricultural management systems that preserve high levels of SOC, in order to improve their sustainability.

Deciphering the weathering processes using environmental mineralogy and geochemistry: Towards an integrated model of laterite and podzol genesis in the Upper Amazon Basin

This article reports major results from collaborative research between France and Brazil on soil and water systems, carried out in the Upper Amazon Basin. It reveals the weathering processes acting in the partly inundated, low elevation plateaus of the Basin, mostly covered by evergreen forest. Our findings are based on geochemical data and mineral spectroscopy that probe the crystal chemistry of Fe and Al in mineral phases (mainly kaolinite, Al- and Fe-(hydr)oxides) of tropical soils (laterites). These techniques reveal crystal alterations in mineral populations of different ages and changes of metal speciation associated with mineral or organic phases. These results provide an integrated model of soil formation and changes (from laterites to podzols) in distinct hydrological compartments of the Amazon landscapes and under altered water regimes. (C) 2010 Academie des sciences. Published by Elsevier Masson SAS. All rights reserved.

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models

The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.

Adapting the CROPGRO perennial forage model to predict growth of Brachiaria brizantha

Warm-season grasses are economically important for cattle production in tropical regions, and tools to aid in management and research of these forages would be highly beneficial. Crop simulation models synthesize numerous physiological processes and are important research tools for evaluating production of warm-season grasses. This research was conducted to adapt the perennial CROPGRO Forage model to simulate growth of the tropical species palisadegrass [Brachiaria brizantha (A. Rich.) Stapf. cv. Xaraes] and to describe model adaptation for this species. In order to develop the CROPGRO parameters for this species, we began with values and relationships reported in the literature. Some parameters and relationships were calibrated by comparison with observed growth, development, dry matter accumulation and partitioning during a 2-year experiment with Xaraes palisadegrass in Piracicaba, SP, Brazil. Starting with parameters for the bahiagrass (Paspalum notatum Flugge) perennial forage model, dormancy effects had to be minimized, and partitioning to storage tissue/root decreased, and partitioning to leaf and stem increased to provide for more leaf and stem growth and less root. Parameters affecting specific leaf area (SLA) and senescence of plant tissues were improved. After these changes were made to the model, biomass accumulation was better simulated, mean predicted herbage yield per cycle was 3573 kg ha(-1), with a RMSE of 538 kg DM ha(-1) (D-Stat = 0.838, simulated/observed ratio = 1.028). The results of the adaptation suggest that the CROPGRO model is an efficient tool to integrate physiological aspects of palisadegrass and can be used to simulate growth. (C) 2010 Elsevier B.V. All rights reserved.

A Log-Linear Regression Model for the Beta-Weibull Distribution

We introduce the log-beta Weibull regression model based on the beta Weibull distribution (Famoye et al., 2005; Lee et al., 2007). We derive expansions for the moment generating function which do not depend on complicated functions. The new regression model represents a parametric family of models that includes as sub-models several widely known regression models that can be applied to censored survival data. We employ a frequentist analysis, a jackknife estimator, and a parametric bootstrap for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Further, for different parameter settings, sample sizes, and censoring percentages, several simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We define martingale and deviance residuals to evaluate the model assumptions. The extended regression model is very useful for the analysis of real data and could give more realistic fits than other special regression models.

Test procedure for variable rate fertilizer on coffee

The objective was to develop and test a procedure for applying variable rates of fertilizers and evaluate yield response in coffee (Coffea arabica L.) with regard to the application of phosphorus and potassium. The work was conducted during the 2004 season in a 6.4 ha field located in central Sao Paulo state. Two treatments were applied with alternating strips of fixed and variable rates during the whole season: one following the fertilizing procedures recommended locally, and the other based on a grid soil sampling. A prototype pneumatic fertilizer applicator was used, carrying two conveyor belts, one for each row. Harvesting was done with a commercial harvester equipped with a customized volumetric yield monitor, separating the two treatments. Data were analyzed based on geostatistics, correlations and regressions. The procedure showed to be feasible and effective. The area that received fertilizer applications at a variable rate showed a 34% yield increase compared to the area that received a fixed rate. The variable rate fertilizer resulted in a savings of 23% in phosphate fertilizer and a 13% increase in potassium fertilizer, when compared to fixed rate fertilizer. Yield in 2005, the year after the variable rate treatments, still presented residual effect from treatments carried out during the previous cycle.