Axel y Garcia

Equações para a estimativa do índice de área foliar do cafeeiro

With the purpose of estimating the temporal variation of the coffee leaf area index (LAI), using a non destructive simple methodology, anxa0experiment was carried out at the Crop Production Department, Escola Superior de Agricultura Luiz de Queiroz, of the Universidade de Sao Paulo, Piracicaba, SP, Brazil. The Mundoxa0Novo IACxa0388-17 cultivar, grafted on the cultivar Apoataxa0IACxa02258 (15 to 35 months old), was used with a distance between plants of 2.5xa0mxa0x 1.0xa0m, where all leaves of two coffee plants were collected, with intervals varying from 60 to 150 days, to measure the leaf area using the LI-COR equipment (model 3100). To obtain the functional relationship between the LAI and different growth variables (plant height, total number and mass of leaves and leaf area) and the canopy architecture (inferior, medium and superior crop canopy area; crop canopy lateral area; inferior, medium and superior canopy diameter; the plant canopy volume; and the first two branches height), a conic shape for aerial plant architecture was assumed. The inferior canopy diameter (first two branches) and the plant height can be used to estimate the coffee leaf area index.

Alternative Crop Insurance Indexes

Three index-based crop insurance contracts are evaluated for representative south Georgia corn farms. The insurance contracts considered are based on indexes of historical county yields, yields predicted from a cooling degree-day production model, and yields predicted from a crop-simulation model. For some of the representative farms, the predicted yield index contracts provide yield risk protection comparable to the contract based on historical county yields, especially at lower levels of risk aversion. The impact of constraints on index insurance choice variables is considered and important interactions among constrained, conditionally optimized, choice variables are analyzed.

Using thermal units for estimating critical period of weed competition in off-season maize crop

Abstract Brazilian off-season maize production is characterized by low yield due to several factors, such as climate variability and inadequate management practices, specifically weed management. Thus, the goal of this study was to determinate the critical period of weed competition in off-season maize (Zea mays L.) crop using thermal units or growing degree days (GDD) approach to characterize crop growth and development. The study was carried out in experimental area of the University of São Paulo, Brazil, with weed control (C), as well as seven coexistence periods, 2, 4, 6, 8, and 12 leaves, flowering, and all crop cycle; fourteen treatments were done. Climate data were obtained from a weather station located close to the experimental area. To determine the critical period for weed control (CPWC) logistic models were fitted to yield data obtained in both W and C, as a function of GDD. For an arbitrary maximum yield loss fixed in 2.5%, the CPWC was found between 301 and 484 GDD (7–8 leaves). Also, when the arbitrary loss yield was fixed in 5 and 10%, the period before interference (PBI) was higher than the critical weed-free period (CWFP), suggesting that the weeds control can be done with only one application, between 144 and 410 GDD and 131 and 444 GDD (3–8 leaves), respectively. The GDD approach to characterize crop growth and development was successfully used to determine the critical period of weeds control in maize sown off-season. Further works will be necessary to better characterize the interaction and complexity of maize sown off-season with weeds. However, these results are encouraging because the possibility of the results to be extrapolated and because the potential of the method on providing important results to researchers, specifically crop modelers.Brazilian off-season maize production is characterized by low yield due to several factors, such as climate variability and inadequate management practices, specifically weed management. Thus, the goal of this study was to determinate the critical period of weed competition in off-season maize (Zea mays L.) crop using thermal units or growing degree days (GDD) approach to characterize crop growth and development. The study was carried out in experimental area of the University of São Paulo, Brazil, with weed control (C), as well as seven coexistence periods, 2, 4, 6, 8, and 12 leaves, flowering, and all crop cycle; fourteen treatments were done. Climate data were obtained from a weather station located close to the experimental area. To determine the critical period for weed control (CPWC) logistic models were fitted to yield data obtained in both W and C, as a function of GDD. For an arbitrary maximum yield loss fixed in 2.5%, the CPWC was found between 301 and 484 GDD (7-8 leaves). Also, when the arbitrary loss yield was fixed in 5 and 10%, the period before interference (PBI) was higher than the critical weed-free period (CWFP), suggesting that the weeds control can be done with only one application, between 144 and 410 GDD and 131 and 444 GDD (3-8 leaves), respectively. The GDD approach to characterize crop growth and development was successfully used to determine the critical period of weeds control in maize sown off-season. Further works will be necessary to better characterize the interaction and complexity of maize sown off-season with weeds. However, these results are encouraging because the possibility of the results to be extrapolated and because the potential of the method on providing important results to researchers, specifically crop modelers.

Container Temperature and Moisture for Estimating Evapotranspiration of Nursery Crops

The availability of water for agriculture has become an important issue, especially due to nthe continuing drought in the southeastern USA and because water is one of the most critical inputs nfor nursery plants. The goal of this study was to estimate the amount of water required for nursery ncrops based on the soil temperature and moisture measurements of nursery containers. The nexperiment was conducted at the Center for Applied Nursery Research (CANR), located in Dearing, nMcDuffie County, Georgia. Fifteen soil temperature probes and fifteen soil moisture probes were ninstalled in three different sizes of containers, including 11.4-, 19.0- and 26.5-L pots filled with a soil nmixture consisting of bark, lime, fertilizer, and sand. The probes were connected to an automated ndata logger, which recorded the container conditions every 15 minutes. At midnight the data logger nalso calculated the daily extremes and averages. This information was retrieved hourly via a ndedicated telephone line and modem by a computer located at the College of Agricultural and Environmental Science-Griffin Campus. At the same time an automatic weather station recorded nseveral weather variables for the same period The experiment was carried out from November 19th n2002 through May 20th 2003. The 11.4-L containers were planted with Ilex chinensis “Bufordi”, Dwarf nBurford, the 19.0-L containers were planted with Cuppressocyparis “Leylandi” and the 26.5-L ncontainers were planted with Ilex x “Ruby Sceptor”. The soil moisture and soil temperature probes nhomogeneity was evaluated through the analysis of the cumulative distribution functions (CDFs) of nthe 15 minute observations. The Kolmogorov-Smirnov two-sample, two-side test was used to ncompare all CDFs combinations by treatment. Kolmogorov-Smirnov’s test can detect all type of ndifferences that might exist between two distribution functions and a statistic test (D) is calculated. nThereafter, simple equation regressions of one and two order were fitted. Container moisture was naffected by the air temperature seasonal variation. High variability and increase of container soil nwater content for days without rainfall and irrigation was observed during the winter. This nphenomenon was probably due to temperature gradient that could transport water from warm to cool nfronts. Meanwhile, the soil moisture probes showed an adequate variation of the soil moisture ncontent during the spring. The experiment received 434mm of water from irrigation and 690mm from nrainfall. The irrigation efficiencies were 44, 40, and 53% and the total water consumption was 193, n173, and 230mm in the 11.4, 19.0, and 26.5-L containers, respectively. Functional relationships nbetween container temperature and moisture with air temperature provided satisfactory evidences for nestimation of water use by nursery crops and could be used for irrigation scheduling to conserve nwater use.

Simulating peanut yield and irrigation applications with the CSM-CROPGRO-Peanut model

The management of on-farm irrigation systems involves the choice of irrigation nmethod, timing, and the quantity of water applications. In Georgia, farmers irrigation napplications are largely unknown because of no reporting requirement. Recent droughts and a nwater dispute with the neighboring states–Alabama and Florida– highlighted the need for an naccurate estimate of water use by agriculture. An accurate simulation of irrigation water use is nneeded to help improve yield predictions and contribute to the resolution of the tri-state water ndispute. The objective of this study was to evaluate the performance of the CSM-CROPGROPeanut nmodel in simulating irrigation applications and its impact on peanut yield in farmers nfields in southwest Georgia. A set of different irrigation thresholds was used to run the CSMCROPGRO- nPeanut model. We then compared the simulated irrigation applications for each of the irrigation thresholds with the amount of water that the farmers actually applied during the n2003 growing season. nWe found the best agreement between simulated and observed irrigation applications with the n50% irrigation threshold. However, the irrigation applications by farmers could be much higher nthan with the 50% irrigation threshold during critical stages of crop growth and development nwhen no adequate rainfall occurs. Similarly, peanut yield was simulated well by the model with nthe 50% irrigation threshold. This study showed that the CSM-CROPGRO-Peanut model can be na useful tool for estimating farmers irrigation applications and its impact on yield. Potential users nof this model could include policy makers, planners, and regulators that deal with water issues.

Irrigation Water Use Estimation with Crop Models and Geostatistical Techniques; the Southeast Climate Consortium Experience

The amount of water used for irrigation varies as a function of the local weather conditions nand soil type, crop and cultivar selection, crop management, and irrigation strategies, including the ntiming and amount of irrigation applications. Among these factors, weather conditions and the soilwater nholding characteristics are often the most important factors that define the spatial variability of nirrigation in a specific region. In Georgia, the amount of water used by agriculture for irrigation is nlargely unknown because of the lack of reporting requirements. Recent droughts and a water dispute nwith the neighboring states, including Alabama and Florida, highlighted the need for an accurate nestimate of water use by agriculture. The goal of this study was to characterize the spatial variability nof the monthly irrigation water use for cotton in Georgia using the Cropping System Model (CSM) of nthe Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.0. Farmers monthly nirrigation applications for cotton during the 2002 growing season were obtained from selected sites of nthe Agricultural Water Pumping program. We selected farmers’ fields that were located within 15 km nfrom the nearest weather station of the Georgia Automated Environmental Monitoring Network or the nCooperative Observer Program (COOP) network of the National Weather Service. We then ncompared the spatial and temporal distribution of irrigation amounts predicted by the CSM model nwith the amount of water that the farmers actually applied. The most successful model for spatial nestimation of monthly total irrigation was the Spherical model, followed by the Exponential model. nThe smaller number of sample sites in certain parts of the study area had a significant impact on nspatial estimates of monthly total irrigation. This study demonstrated the potential of using a crop nmodel combined with geostatistical techniques for estimation of regional water use.