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Crop irrigation requirements are usually estimated based on crop evapotranspiration (ETc) as determined by the reference evapotranspiration (ETo) and crop coefficient (Kc). There is a lack of knowledge on the irrigation requirements of tropical forage crops in Brazil, contrasting with the increasing use of irrigation in pastures. The effort of this study was to investigate what would be the water needs of tropical forages in Southern Brazil, based on a robust experimental database. The study was carried out in São Paulo State-Brazil using different forages species and their combinations [Guinea grass (GG); Guinea grass + black oat + ryegrass (GOR); Bermuda grass (BG), and Bermuda + black oat + ryegrass (BOR)]. The experimental fields were fully irrigated, and the Kc values were derived from ETc measurements on lysimeters; ETo was estimated using daily data from a nearby weather station and the standard FAO56 parameterization. Mean daily ETc values for GG, GOR, BG and BOR were 4.1, 2.9, 3.6, and 3.4 mm, respectively, and respective mean Kc values were 0.99, 0.90, 1.0, and 0.94. Average Kc values for all plots decreased as ETo increased, producing a negative Kc-ETo relationship, mainly when ETo reached values greater than 5 mm d−1. This was most likely due to internal plant stomatal resistance to vapor release from the leaves diffusing to the atmosphere at high ETo. So, the time-based Kc curves described by FAO 56 manual should be adjusted for the analyzed crops considering different ranges of ETo to improve the required irrigation depth. [ABSTRACT FROM AUTHOR]

Process‐based models such as the Agricultural Production Systems Simulator (APSIM) can be useful to simulate pasture growth and development. This aim of the study is to parameterise the APSIM‐Tropical Pasture model to simulate Marandu palisade grass (Urochloa brizantha cv. Marandu), irrigated and rainfed, under cut‐and‐carry management. Parameterisation was performed with experimental data collected in Piracicaba, SP, Brazil (Experiment 1: 11 growth cycles), and São Carlos, SP, Brazil (Experiment 2: 9 growth cycles). In both experiments, aboveground live biomass, leaf, stem, crown and dead material mass, specific leaf area (SLA), leaf area index (LAI) and height were assessed. In Experiment 1, soil water content was measured with a capacitance probe, at depths from 0 to 0.9 m. Calibration was performed for base temperature, light extinction coefficient and radiation use efficiency. The calibrated model proved to be accurate in simulating living biomass, leaf mass, LAI and height for all tested variables. In the irrigated system, the statistics presented values between 0.64 and 0.86 for coefficient of determination (R2), between 0.59 and 0.70 for efficiency coefficient (NSE) and between 0.88 and 0.95 for the Willmott's agreement index (d). For these same variables, in the rainfed system, R2 values ranged from 0.70 to 0.86; NSE from 0.54 to 0.70; and average d was 0.93. The simulation of soil water content showed satisfactory results for rainfed pasture, but was less precise for the irrigated treatment. The calibration of the APSIM‐Tropical Pasture model for Marandu palisade grass resulted in efficient simulations of pasture growth under cut‐and‐carry management. [ABSTRACT FROM AUTHOR]

Considering profitability in pasture-based systems, investigating parameters affecting crop coefficients for irrigation management becomes important. In this experiment, we determined the crop coefficient of 'Marandu' palisadegrass based on accumulated degree-days and estimated plant water consumption under single ('Marandu' palisadegrass) and mixed ('Marandu' palisadegrass + black oats + Italian ryegrass) cropping regimes. The research was conducted at the Luiz de Queiroz College of Agriculture in Piracicaba, São Paulo, Brazil, between 2016 and 2017. Evapotranspiration was assessed using weighing lysimeters while crop evapotranspiration was calculated using mean weight variation. Reference evapotranspiration and degree-days were estimated. Data were obtained from an automated weather station. Equations and regression models relating crop coefficient with accumulated degree-days were generated for two seasons (spring/summer and autumn/winter) and evaluated for two year-cycles, from 2015 to 2018. The results showed better prediction accuracy for the single cropping system in spring/summer 2017-18. [ABSTRACT FROM AUTHOR]