Suchergebnisse

Abstract. The evapotranspiration-based scheduling method is the most common method for irrigation programming in agriculture. There is no doubt that the estimation of the reference evapotranspiration (ETo) is a key factor in irrigated agriculture. However, the high cost and maintenance of agrometeorological stations and high number of sensors required to estimate it make it non-plausible, especially in rural areas. For this reason, the estimation of ETo using air temperature, in places where wind speed, solar radiation and air humidity data are not readily available, is particularly attractive. A daily data record of 49 stations distributed over Duero basin (Spain), for the period 2000–2018, was used for estimation of ETo based on seven models against Penman–Monteith (PM) FAO 56 (FAO – Food and Agricultural Organization of the United Nations) from a temporal (annual or seasonal) and spatial perspective. Two Hargreaves–Samani (HS) models, with and without calibration, and five Penman–Monteith temperature (PMT) models were used in this study. The results show that the models' performance changes considerably, depending on whether the scale is annual or seasonal. The performance of the seven models was acceptable from an annual perspective (R2>0.91, NSE > 0.88, MAE < 0.52 and RMSE < 0.69 mm d−1; NSE – Nash–Sutcliffe model efficiency; MAE – mean absolute error; RMSE – root-mean-square error). For winter, no model showed good performance. In the rest of the seasons, the models with the best performance were the following three models: PMTCUH (Penman–Monteith temperature with calibration of Hargreaves empirical coefficient – kRS, average monthly value of wind speed, and average monthly value of maximum and minimum relative humidity), HSC (Hargreaves–Samani with calibration of kRS) and PMTOUH (Penman–Monteith temperature without calibration of kRS, average monthly value of wind speed and average monthly value of maximum and minimum relative humidity). The HSC model presents a calibration of the Hargreaves empirical coefficient (kRS). In the PMTCUH model, kRS was calibrated and average monthly values were used for wind speed and maximum and minimum relative humidity. Finally, the PMTOUH model is like the PMTCUH model except that kRS was not calibrated. These results are very useful for adopting appropriate measures for efficient water management, especially in the intensive agriculture in semi-arid zones, under the limitation of agrometeorological data.

Abstract. Vegetation indices based on satellite images, such as the normalized difference vegetation index (NDVI), have been used in countries like the USA, Canada and Spain for damaged pasture and forage insurance over the last few years. This type of agricultural insurance is called satellite-index-based insurance (SIBI). In SIBI, the occurrence of damage is defined as normal distributions. In this work a pasture area at the north of the Community of Madrid (Spain) has been delimited by means of Moderate Resolution Imaging Spectroradiometer (MODIS) images. A statistical analysis of NDVI histograms was applied to seek for alternative distributions using the maximum likelihood method and χ2 test. The results show that the normal distribution is not the optimal representation and the generalized extreme value (GEV)
distribution presents a better fit through the year based on a quality
estimator. A comparison between normal and GEV is shown with respect to the
probability under a NDVI threshold value throughout the year. This suggests that
an a priori distribution should not be selected and a percentile methodology
should be used to define a NDVI damage threshold rather than the average and
standard deviation, typically of normal distributions. Highlights.
The GEV distribution provides better fit to the NDVI historical observations than the normal one.
Differences between normal and GEV distributions are higher during spring and autumn, which are transition periods in the precipitation regimen. NDVI damage threshold shows evident differences using normal and GEV distributions both covering the same probability (24.20 %). NDVI damage threshold values based on percentile calculation are proposed as an improvement in the index-based insurance in damaged pasture.