ABSTRACT
Studies on the use of deficit irrigation and application of models for estimating agronomic performance of crops can help in more sustainable agricultural managements. The objective of this study was to evaluate the effect of irrigation levels on the agronomic performance of white oat (Avena sativa L.) and accuracy of the CERES-Barley model in simulating white oat growth and yield, as well as performing long-term simulation to identify the best sowing time for each irrigation management. The experiment consisted of five irrigation levels (11%, 31%, 60%, 87%, and 100%), being conducted in two agricultural years in southeastern Brazil. The model was calibrated with data of the treatment without water deficit (100%) of the first year and validated with the data of the other treatments in the 2 years. Long-term analyses, with a historical series of 16 years, were performed to recommend the best sowing dates for each irrigation management. Deficit irrigation linearly reduces the agronomic performance of white oat. The high accuracy of white oat yield estimation (R2 = 0.86; RMSE = 616 kg ha-1) using the CERES-Barley model allowed the long-term simulation for establishing the best sowing date for each irrigation level. For higher irrigation levels, sowing in periods with lower temperature (May and June) is more appropriate, as the 1 °C increment in the average temperature before flowering reduces crop yield by 600 kg ha-1. At irrigation levels with higher deficit, sowing in periods with higher rainfall (March and April) promotes higher crop yield.
Subject(s)
Avena , Hordeum , Agricultural Irrigation , Agriculture , Crops, Agricultural , Edible GrainABSTRACT
ABSTRACT Soil CO2 emissions (fCO2) in agricultural areas have been widely studied in global climate change research, but its characterization and quantification are restricted to small areas. Because spatial and time variability affect emissions, tools need to be developed to predict fCO2 for large areas. This study aimed to investigate soil magnetic susceptibility (MS) and its correlation with fCO2 in an agricultural environment. The experiment was carried out on a Typic Eutrudox located in Guariba-SP, Brazil. Results showed that there was negative spatial correlation between fCO2 and the magnetic susceptibility of Air Dried Soil (MSADS) up to 34.3 m distant. However, the fCO2 had no significant correlation with MSADS, magnetic susceptibility of sand (MSSAND) nor clay (MSCLAY). However, MSADS could be a supplemental mean of identifying regions of high fCO2 potential over large areas.(AU)
Subject(s)
Agricultural Zones/analysis , Carbon Dioxide/analysis , Carbon Dioxide/chemistry , Carbon Dioxide/classification , Magnetic FieldsABSTRACT
ABSTRACT Soil CO2 emissions (fCO2) in agricultural areas have been widely studied in global climate change research, but its characterization and quantification are restricted to small areas. Because spatial and time variability affect emissions, tools need to be developed to predict fCO2 for large areas. This study aimed to investigate soil magnetic susceptibility (MS) and its correlation with fCO2 in an agricultural environment. The experiment was carried out on a Typic Eutrudox located in Guariba-SP, Brazil. Results showed that there was negative spatial correlation between fCO2 and the magnetic susceptibility of Air Dried Soil (MSADS) up to 34.3 m distant. However, the fCO2 had no significant correlation with MSADS, magnetic susceptibility of sand (MSSAND) nor clay (MSCLAY). However, MSADS could be a supplemental mean of identifying regions of high fCO2 potential over large areas.