Irrigation water is limited and scarce in many areas of the world, including Comarca Lagunera, Mexico. Thus better estimations of irrigation water requirements are essential to conserve water. The general objective was to estimate crop water demands or crop evapotranspiration (ET c ) at different scales using satellite remote sensing-based vegetation index. The study was carried out in northern Mexico (Comarca Lagunera) during four growing seasons. Six, eleven, three, and seven clear Landsat images were acquired for 2013, 2014, 2015, and 2016, respectively, for the analysis. The results showed that ET c was low at initial and early development stages, while ET c was high during mid-season and harvest stages. These results are not new but give us confidence in the rest of our ET c results. Daily ET c maps helped to explain the variability of crop water use during the growing season. Based on the results we can conclude that ET c maps developed from remotely sensed multispectral vegetation indices are a useful tool for quantifying crop water consumption at regional and field scales. Using ET c maps at the field scale, farmers can supply appropriate amounts of irrigation water corresponding to each growth stage, leading to water conservation.
Accurate estimation of crop evapotranspiration (ET) is a key factor in agricultural water management including irrigated agriculture. The objective of this study was to compare ET estimated from the satellite-based remote sensing METRIC model to in situ atmometer readings. Atmometer readings were recorded from three sites in eastern South Dakota every morning between 8:15 and 8:30 AM for the duration of the 2016 growing season. Seven corresponding clear sky images from Landsat 7 and Landsat 8 (Path 29, Row 29) were processed and used for comparison. Three corn fields in three sites were used to compare actual evapotranspiration (ET a ). The results showed a good relationship between ET a estimated by the METRIC model (ET a -METRIC) and ET a estimated with atmometer (ET a -atm) ( 2 = 0.87, index of agreement of 0.84, and RMSE = 0.65 mm day −1 ). However, ET a -atm values were consistently lower than ET a -METRIC values. The differences in daily ET a between the two methods increase with high wind speed values (>4 m s −1 ). Results from this study are useful for improving irrigation water management at local and field scales.
<p><strong>Background:</strong> Zinc (Zn) is an important element in human health and is consumed through foods of animal origin. However, the biofortification of plants with Zn can be a strategy for the consumption of this micronutrient and to increase the morphology, physiology, and plant yield. <strong>Objective:</strong> Quantify the effect of Zn application on yield, nutraceutical quality and antioxidant activity of lettuce. <strong>Methodology:</strong> Foliar application of ZnSO<sub>4 </sub>(0, 25, 50, 75 and 100 µM L<sup>-1</sup>) on lettuce plants was made. Yield, nutraceutical quality and the concentration of Zn in the plant tissue was determinate. <strong>Results:</strong> The optimum Zn dose that maximized yield and nutraceutical quality, as well as the recommended consumption concentration in lettuce in this study was 75 µM L<sup>-1</sup> (ZnSO<sub>4</sub>). <strong>Implications: </strong>Higher doses of Zn decreased bioactive compound biosynthesis. <strong>Conclusion:</strong> Zn biofortification is an alternative to increase phytochemical compound biosynthesis and yield with the possibility of improving public health.</p>
The verification of remotely sensed estimates of surface variables is essential for any remote sensing study. The objective of this study was to compare leaf area index (LAI), surface temperature (Ts), and actual evapotranspiration (ETa), estimated using the remote sensing-based METRIC model and in situ measurements collected at the satellite overpass time. The study was carried out at a commercial corn field in eastern South Dakota. Six clear-sky images from Landsat 7 and Landsat 8 (Path 29, Row 29) were processed and used for the assessment. LAI and Ts were measured in situ, and ETa was estimated using an atmometer and independent crop coefficients. The results revealed good agreement between the variables measured in situ and estimated by the METRIC model. LAI showed r2 = 0.76, and RMSE = 0.59 m2 m−2, the Ts comparison had an agreement of r2 = 0.87 and RMSE 1.24 °C, and ETa presented r2 = 0.89 and RMSE = 0.71 mm day−1.
The objective was to evaluate the effects of two (2×) vs three (3×) times per day milking on milk production and milk composition in dairy cows. Fourteen scientific papers, containing production data from 16 trials, where dairy cows were milked 2× or 3×, were analysed using meta-analysis with fixed and random-effects with the R statistical program. The degree of heterogeneity and publication bias were measured with the I2 statistic and Begg’s test, respectively. In addition, the meta-regression analysis explored other sources of heterogeneity for the response. The estimated effect size of 2× and 3× milkings was calculated for dry matter intake (DMI), milk production, and milk composition. Dry matter intake, milk production, and milk fat and protein yields showed substantial heterogeneity (I2>50%). Whereas milk fat-percentage had moderate heterogeneity (I2<50%), and milk protein had no (I2=0%)heterogeneity. The year of publication, trial duration, and cattle breed did not influence production response parameters to milking frequency. We found no evidence of publication bias for the parameters evaluated (Begg’s test; P>.05). Cows milked 2× produced less milk (2.23 kg/d), less milk fat (0.06kg/d), and less milk protein (0.05 kg/d). In contrast, the fat percentage was lower (0.07 units) in 3×, compared with 2× milking frequency. There was no effect of milking frequency on DMI and milk protein percentage. In conclusion, milk production and milk fat and protein yields improves as milking frequency increase from 2× to 3× daily, without affecting DMI. The implementation of 3× milking frequency must consider dairy cow management, labor, and milking parlour infrastructure, particular to each dairy farm.
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