תפריט נגישות
ניגודיות עדינהניגודיות גבוהה
מונוכרום
הדגשת קישורים
חסימת אנימציה
פונט קריא
סגור
איפוס הגדרות נגישות
להורדת מודול נגישות חינם תפריט נגישותניגודיות עדינהניגודיות גבוההמונוכרוםהדגשת קישוריםחסימת אנימציהפונט קריאסגוראיפוס הגדרות נגישותלהורדת מודול נגישות חינםCurrent irrigation management zones (IMZs) for variable rate irrigation (VRI) systems are static. They are delineated in the beginning of the season and used thereafter. However, recent research has shown that IMZ boundaries are transient and change with time during the growing season. The primary goal of this study was to explore the potential of using vegetation indices (VIs) developed from unmanned aerial vehicle (UAV) and satellite images to predict cotton physiological parameters that can be used to delineate in-season boundaries of IMZs. A 2 year study was conducted in a 38 ha commercial cotton field in southwestern Georgia, USA. Throughout the two growing seasons, VIs were calculated from UAV, PlanetScope, and Sentinel-2 images. Predawn leaf water potential (LWPPD) and plant height were measured at 37 locations in the field on the same day as the flights and correlated with UAV and satellite based-VIs. GNDVI (Green normalized difference vegetation index) was the best predictor of plant height with correlation values of 0.72 (p < .0001) and 0.84 (p < .0001) for 2019 and 2020, respectively. A secondary goal was to compare the performance of dynamic VRI (DVRI) to conventional irrigation. The field was divided into alternating parallel conventional, and DVRI strips to compare the two scheduling methods. The conventional strips were irrigated using the farmer’s standard method and individual IMZs within the DVRI strips were irrigated based on soil water tension (SWT) measured with a wireless soil moisture sensor network. LWP and SWT measurements correlated well. IMZs were initially delineated using soil texture, apparent soil electrical conductivity (ECa), and yield maps and satellite images from previous years and were modified in-season to reflect patterns observed in the plant height maps. In 2020, the DVRI system prescribed an average irrigation amount of 50.8 mm while conventional irrigation applied an average of 58.4 mm. Average yields for DVRI and conventional were 1248 and 1191 kg ha−1, respectively. The DVRI system resulted in average yield 4.6% higher than conventional irrigation, while applying 14.0% less water. Despite the lower water application by the DRVI system, the performance comparison between the DRVI and the conventional irrigation was not conclusive.
Current irrigation management zones (IMZs) for variable rate irrigation (VRI) systems are static. They are delineated in the beginning of the season and used thereafter. However, recent research has shown that IMZ boundaries are transient and change with time during the growing season. The primary goal of this study was to explore the potential of using vegetation indices (VIs) developed from unmanned aerial vehicle (UAV) and satellite images to predict cotton physiological parameters that can be used to delineate in-season boundaries of IMZs. A 2 year study was conducted in a 38 ha commercial cotton field in southwestern Georgia, USA. Throughout the two growing seasons, VIs were calculated from UAV, PlanetScope, and Sentinel-2 images. Predawn leaf water potential (LWPPD) and plant height were measured at 37 locations in the field on the same day as the flights and correlated with UAV and satellite based-VIs. GNDVI (Green normalized difference vegetation index) was the best predictor of plant height with correlation values of 0.72 (p < .0001) and 0.84 (p < .0001) for 2019 and 2020, respectively. A secondary goal was to compare the performance of dynamic VRI (DVRI) to conventional irrigation. The field was divided into alternating parallel conventional, and DVRI strips to compare the two scheduling methods. The conventional strips were irrigated using the farmer’s standard method and individual IMZs within the DVRI strips were irrigated based on soil water tension (SWT) measured with a wireless soil moisture sensor network. LWP and SWT measurements correlated well. IMZs were initially delineated using soil texture, apparent soil electrical conductivity (ECa), and yield maps and satellite images from previous years and were modified in-season to reflect patterns observed in the plant height maps. In 2020, the DVRI system prescribed an average irrigation amount of 50.8 mm while conventional irrigation applied an average of 58.4 mm. Average yields for DVRI and conventional were 1248 and 1191 kg ha−1, respectively. The DVRI system resulted in average yield 4.6% higher than conventional irrigation, while applying 14.0% less water. Despite the lower water application by the DRVI system, the performance comparison between the DRVI and the conventional irrigation was not conclusive.
