Evapotranspiration Rates of Three Sweet Corn Cultivars under Different Irrigation Levels

Understanding plants’ response to different irrigation levels is essential for developing effective irrigation scheduling practices that conserve water without affecting plant growth and yield. The objective of this study was to evaluate the responses of three sweet corn (Zea mays var. saccharata) cultivars 1170, 8021, and Battalion under three irrigation levels (50%, 75%, and 100%). Irrigation treatments were based on soil moisture management allowable depletion. Replicated trials were conducted, in an open field using 1-gal containers, at the Tropical Research and Education Center, Homestead, FL. A drip system with microsprinklers was used for irrigation. Daily crop evapotranspiration (ETc) rates were measured using a digital scale based on differences in weights of soil containers and plants. Reference evapotranspiration (ETo) was calculated using the FAO-Penman-Monteith equation. Crop-coefficient (Kc) values for the three cultivars were calculated from measured ETc and calculated ETo. In addition, leaf area, stomatal conductance, and fresh biomass were measured. Total irrigation amounts corresponding to the 50%, 75%, and 100% treatments were 116, 162, and 216 mm, and total ETc values were 128, 157, and 170 mm, respectively. The two deficit irrigation treatments (50% and 75%) resulted in a reduction of ETc for the three cultivars compared with the 100% irrigation treatments. Results also showed that under 75% and 100% treatments, Kc values were usually greater than 1 for the three cultivars and reached as high as 1.5. Additionally, leaf area and fresh biomass weight in the 50% treatment were mostly lower than in the 75% or 100% treatments

Soil water content sensor response to organic matter content under laboratory conditions

Studies show that the performance of soil water content monitoring (SWCM) sensors is affected by soil physical and chemical properties. However, the effect of organic matter on SWCM sensor responses remains less understood. Therefore, the objectives of this study are to (i) assess the effect of organic matter on the accuracy and precision of SWCM sensors using a commercially available soil water content monitoring sensor; and (ii) account for the organic matter effect on the sensor’s accuracy. Sand columns with seven rates of oven-dried sawdust (2%, 4%, 6%, 8%, 10%, 12% and 18% v/v, used as an organic matter amendment), thoroughly mixed with quartz sand, and a control without sawdust were prepared by packing quartz sand in two-liter glass containers. Sand was purposely chosen because of the absence of any organic matter or salinity, and also because sand has a relatively low cation exchange capacity that will not interfere with the treatment effect of the current work. Sensor readings (raw counts) were monitored at seven water content levels (0, 0.02, 0.04, 0.08, 0.12, 0.18, 0.24, and 0.30 cm3 cm-3) by uniformly adding the corresponding volumes of deionized water in addition to the oven-dry one. Sensor readings were significantly (p \u3c 0.05) affected by the organic matter level and water content. Sensor readings were strongly correlated with the organic matter level (R2 = 0.92). In addition, the default calibration equation underestimated the water content readings at the lower water content range (\u3c0.05 cm3 cm-3), while it overestimated the water content at the higher water content range (\u3e0.05 cm3 cm-3). A new polynomial calibration equation that uses raw count and organic matter content as covariates improved the accuracy of the sensor (RMSE = 0.01 cm3 cm-3). Overall, findings of this study highlight the need to account for the effect of soil organic matter content to improve the accuracy and precision of the tested sensor under different soils and environmental conditions

Analysis of Potential Future Climate and Climate Extremes in the Brazos Headwaters Basin, Texas

Texas’ fast-growing economy and population, coupled with cycles of droughts due to climate change, are creating an insatiable demand for water and an increasing need to understand the potential impacts of future climates and climate extremes on the state’s water resources. The objective of this study was to determine potential future climates and climate extremes; and to assess spatial and temporal changes in precipitation (Prec), and minimum and maximum temperature (Tmin and Tmax, respectively), in the Brazos Headwaters Basin under three greenhouse gas emissions scenarios (A2, A1B, and B1) for three future periods: 2020s (2011–2030), 2055s (2046–2065), and 2090s (2080–2099). Daily gridded climate data obtained from Climate Forecast System Reanalysis (CFSR) were used to downscale outputs from 15 General Circulation Models (GCMs) using the Long Ashton Research Station–Weather Generator (LARS-WG) model. Results indicate that basin average Tmin and Tmax will increase; however, annual precipitation will decrease for all periods. Annual precipitation will decrease by up to 5.2% and 6.8% in the 2055s and 2090s, respectively. However, in some locations in the basin, up to a 14% decrease in precipitation is projected in the 2090s under the A2 (high) emissions scenario. Overall, the northwestern and southern part of the Brazos Headwaters Basin will experience greater decreases in precipitation. Moreover, precipitation indices of the number of wet days (prec ≥ 5 mm) and heavy precipitation days (prec ≥ 10 mm) are projected to slightly decrease for all future periods. On the other hand, Tmin and Tmax will increase by 2 and 3 °C on average in the 2055s and 2090s, respectively. Mostly, projected increases in Tmin and Tmax will be in the upper range in the southern and southeastern part of the basin. Temperature indices of frost (Tmin < 0 °C) and ice days (Tmax < 0 °C) are projected to decrease, while tropical nights (Tmin > 20 °C) and summer days (Tmax > 25 °C) are expected to increase. However, while the frequency distribution of metrological drought shows slight shifts towards the dry range, there was no significant difference between the baseline and projected metrological drought frequency and severity

Soil Water Content Sensor Response to Organic Matter Content under Laboratory Conditions

Studies show that the performance of soil water content monitoring (SWCM) sensors is affected by soil physical and chemical properties. However, the effect of organic matter on SWCM sensor responses remains less understood. Therefore, the objectives of this study are to (i) assess the effect of organic matter on the accuracy and precision of SWCM sensors using a commercially available soil water content monitoring sensor; and (ii) account for the organic matter effect on the sensor’s accuracy. Sand columns with seven rates of oven-dried sawdust (2%, 4%, 6%, 8%, 10%, 12% and 18% v/v, used as an organic matter amendment), thoroughly mixed with quartz sand, and a control without sawdust were prepared by packing quartz sand in two-liter glass containers. Sand was purposely chosen because of the absence of any organic matter or salinity, and also because sand has a relatively low cation exchange capacity that will not interfere with the treatment effect of the current work. Sensor readings (raw counts) were monitored at seven water content levels (0, 0.02, 0.04, 0.08, 0.12, 0.18, 0.24, and 0.30 cm3 cm−3) by uniformly adding the corresponding volumes of deionized water in addition to the oven-dry one. Sensor readings were significantly (p < 0.05) affected by the organic matter level and water content. Sensor readings were strongly correlated with the organic matter level (R2 = 0.92). In addition, the default calibration equation underestimated the water content readings at the lower water content range (<0.05 cm3 cm−3), while it overestimated the water content at the higher water content range (>0.05 cm3 cm−3). A new polynomial calibration equation that uses raw count and organic matter content as covariates improved the accuracy of the sensor (RMSE = 0.01 cm3 cm−3). Overall, findings of this study highlight the need to account for the effect of soil organic matter content to improve the accuracy and precision of the tested sensor under different soils and environmental conditions

Analysis of potential future climate and climate extremes in the brazos headwaters Basin, Texas

Texas\u27 fast-growing economy and population, coupled with cycles of droughts due to climate change, are creating an insatiable demand for water and an increasing need to understand the potential impacts of future climates and climate extremes on the state\u27s water resources. The objective of this study was to determine potential future climates and climate extremes; and to assess spatial and temporal changes in precipitation (Prec), and minimum and maximum temperature (Tmin and Tmax, respectively), in the Brazos Headwaters Basin under three greenhouse gas emissions scenarios (A2, A1B, and B1) for three future periods: 2020s (2011-2030), 2055s (2046-2065), and 2090s (2080-2099). Daily gridded climate data obtained from Climate Forecast System Reanalysis (CFSR) were used to downscale outputs from 15 General Circulation Models (GCMs) using the Long Ashton Research Station-Weather Generator (LARS-WG) model. Results indicate that basin average Tmin and Tmax will increase; however, annual precipitation will decrease for all periods. Annual precipitation will decrease by up to 5.2% and 6.8% in the 2055s and 2090s, respectively. However, in some locations in the basin, up to a 14% decrease in precipitation is projected in the 2090s under the A2 (high) emissions scenario. Overall, the northwestern and southern part of the Brazos Headwaters Basin will experience greater decreases in precipitation. Moreover, precipitation indices of the number of wet days (prec ≥ 5 mm) and heavy precipitation days (prec ≥ 10 mm) are projected to slightly decrease for all future periods. On the other hand, Tmin and Tmax will increase by 2 and 3 °C on average in the 2055s and 2090s, respectively. Mostly, projected increases in Tmin and Tmax will be in the upper range in the southern and southeastern part of the basin. Temperature indices of frost (Tmin \u3c 0 °C) and ice days (Tmax \u3c 0 °C) are projected to decrease, while tropical nights (Tmin \u3e 20 °C) and summer days (Tmax \u3e 25 °C) are expected to increase. However, while the frequency distribution of meteorological drought shows slight shifts towards the dry range, there was no significant difference between the baseline and projected meteorological drought frequency and severity

Soil water content sensor response to organic matter content under laboratory conditions

Studies show that the performance of soil water content monitoring (SWCM) sensors is affected by soil physical and chemical properties. However, the effect of organic matter on SWCM sensor responses remains less understood. Therefore, the objectives of this study are to (i) assess the effect of organic matter on the accuracy and precision of SWCM sensors using a commercially available soil water content monitoring sensor; and (ii) account for the organic matter effect on the sensor’s accuracy. Sand columns with seven rates of oven-dried sawdust (2%, 4%, 6%, 8%, 10%, 12% and 18% v/v, used as an organic matter amendment), thoroughly mixed with quartz sand, and a control without sawdust were prepared by packing quartz sand in two-liter glass containers. Sand was purposely chosen because of the absence of any organic matter or salinity, and also because sand has a relatively low cation exchange capacity that will not interfere with the treatment effect of the current work. Sensor readings (raw counts) were monitored at seven water content levels (0, 0.02, 0.04, 0.08, 0.12, 0.18, 0.24, and 0.30 cm3 cm-3) by uniformly adding the corresponding volumes of deionized water in addition to the oven-dry one. Sensor readings were significantly (p \u3c 0.05) affected by the organic matter level and water content. Sensor readings were strongly correlated with the organic matter level (R2 = 0.92). In addition, the default calibration equation underestimated the water content readings at the lower water content range (\u3c0.05 cm3 cm-3), while it overestimated the water content at the higher water content range (\u3e0.05 cm3 cm-3). A new polynomial calibration equation that uses raw count and organic matter content as covariates improved the accuracy of the sensor (RMSE = 0.01 cm3 cm-3). Overall, findings of this study highlight the need to account for the effect of soil organic matter content to improve the accuracy and precision of the tested sensor under different soils and environmental conditions

A multi-satellite approach for water storage monitoring in an arid watershed

The objective of this study was to use satellite imagery to monitor the water budget of Al Ain region in the United Arab Emirates (UAE). Inflows and outflows were estimated and the trend of water storage variation in the study area was examined from 2005 to 2014. Evapotranspiration was estimated using the simplified Penman-Monteith equation. Landsat images were used to determine the extent of agricultural and green areas. Time series of gravity recovery and climate experiment (GRACE) observations over the study area were used to assess the inferred water storage variation from satellite data. The change of storage inferred from the Water Budget Equation showed a decreasing trend at an average rate of 2.57 Mm3 annually. Moreover, GRACE readings showed a decreasing trend at a rate of 0.35 cm of water depth annually. Mann-Kendal, a non-parametric trend test, proved the presence of significant negative trends in both time series at a 5% significance level. A two-month lag resulted in a better agreement (R2 = 0.55) between the change in water storage and GRACE anomalies within the study area. These results suggest that water storage in the study area is being depleted significantly. Moreover, the potential of remote sensing in water resource management, especially in remote and arid areas, was demonstrated

Lessons from a Landscape Irrigation Rebate Program in Miami Dade County

We calculated savings in outdoor water uses from 37 properties in Fisher Island, Florida, that were retrofitted with smart Evapotranspiration-based irrigation controllers through the Miami Dade County’s Landscape Irrigation Rebate Program. We found average water savings of 11.4 million gallons per year from the 37 properties on the island. We discuss the roles of extension personnel in developing and effectively managing an irrigation rebate program and the implications of results from this program for large scale efforts towards efficient use of freshwater resources