Irrigation Scheduling for Cotton Using Soil Moisture Sensors, Smartphone Apps, and Traditional Methods (original) (raw)
Related papers
AGRONOMY AND SOILS Using Precipitation Forecasts to Irrigate Cotton
2015
In this experiment, precipitation forecasts were used to schedule irrigation for cotton (Gossypium hirsutum L.). Four irrigation treatments and two cotton varieties were evaluated at Stripling Irrigation Research Park located near Camilla, GA in 2014. Two treatments were irrigated based on forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) and The Weather Channel ® 's mobile app. Irrigation amounts for these two treatments were determined by the Cotton Irrigation Schedule Suggested for High Yields (check-book recommendations). The third treatment was irrigated by the checkbook method and the fourth was rainfed. Irrigation applied for each treatment was 34.8 cm, 26.7cm, and 31.9 cm for the ECMWF EPS, weather.com, and checkbook, respectively. Rainfed cotton received 16.56 cm in precipitation. All irrigation methods resulted in significantly higher yields than the rainfed cotton. Results suggest using precipitation forecasts to schedule irrigation could provide a convenient alternative to the checkbook method. R ecent droughts throughout the country and the continuing water disputes among the states of Georgia, Alabama, and Florida have made agricultural producers more aware of the importance of managing irrigation systems efficiently. Some southeastern states are beginning to consider laws that will require monitoring and regulation of water used for irrigation. In fact, Georgia recently suspended issuing irrigation permits in some areas to try and limit the amount of water used in irrigation (Hollis, 2013). Even in southwest Georgia, which receives on average 59.06 cm (23.25 in) of rain during the grow
Cell phone app to optimize irrigation cycles for US crops
Meteorological Applications
Evaporation losses can be reduced by adapting a scheduled irrigation time and having a precise irrigation cycle, but owing to high costs, water optimization tools have thus far been available only to large firms. The study presents all the required steps to develop a smart phone app that can predict the optimal irrigation times for a future 15 days on a sample of 80 crops at any location in the United States. The app uses a soil water balance approach together with data provided by weather sites via application programming interfaces (API). This low-cost, portable, water irrigation planner only requires the entry of both crop and sprinkler type in order to enhance greatly small farms' and private households' ability to lower their consumption of water and the associated costs.
Irrigation Scheduling to Promote Corn Productivity in Central Alabama
Journal of agricultural science, 2020
Agriculture is the largest consumer of water in the United States. Results from previous studies have shown that it is possible to substantially reduce irrigation amounts and maintain corn yield. The objectives of this study were to evaluate the advantages and disadvantages of two irrigation scheduling methods for corn production in Alabama. Two irrigation scheduling methods evaluated were: a) Checkbook, which is one of the conventional methods used by farmers that is based on the soil water balance estimated using water lost by evapotranspiration and its replacement through rainfall or irrigation, and b) Sensor-based, which was based on soil matric potential values recorded by soil moisture tension sensors installed in the field. The experimental field was divided into two irrigation management zones (zone A and zone B) based on soil properties of each field. During the 2014 season in zone A, significant grain yield differences were observed between the two irrigation methods. The Checkbook plots exhibited greater yield than Sensor-based plots: 10181 kg ha-1 and 9696 kg ha-1 , respectively. The greater yield on the Checkbook plots could be associated with higher irrigation rate applied, 148 mm more, compared with the Sensor-based plots. In zone B, there were no significant yield differences between both irrigation methods; however, Sensor-based plots out yielded Checkbook plots, with 9673 kg ha-1 and 9584 kg ha-1 , respectively. Even though the irrigation amount applied in Checkbook located in zone B was higher, 102 mm more, there were no significant yield differences. Therefore, it suggests that the Sensor-based method was promissory irrigation scheduling strategy under the conditions of zone B. In 2015, there were no significant grain yield differences between zone A and zone B when the data from the Checkbook plots were analyzed. However, the Sensor-based treatment produced a statistically significant difference of grain yield of 13597 kg ha-1 in zone A and 11659 kg ha-1 in zone B, also both zones received the same amount of irrigation. Overall results of both growing seasons indicated that the use of the Sensor-based irrigation scheduling treatment resulted in similar values of total profit per hectare when compared to Checkbook method. The Sensor-based method seems a promising strategy that could result in water and financial savings, but more research is required.
Sensor Strategies for Scheduling Irrigation in Louisiana
2017
The objective of this study was to evaluate multiple types of soil moisture sensors to determine their applicability for producers in Louisiana agriculture. Irrigation treatments were determined using: A) soil matric potential sensor system, B) volumetric water content sensor system, or C) weekly irrigation depending on rainfall. Overall, both soil moisture sensor systems were capable of limiting irrigation events compared to weekly irrigation during dry periods with 70% water savings without yield reduction occurring at one location. Though accuracy in sensor readings declined over time, they were still helpful in determining trends in soil moisture. However, using a static threshold to trigger irrigation events was not advisable for either sensor system due to their inaccuracy. Proper implementation requires that the producer has the knowledge to interpret the soil moisture data in reference to the physical system for best management practices.
Agricultural Water Management, 2019
The Texas High Plains (THP) region, a vital part of U.S. grain and fiber production, is experiencing the effects of conflicting interests in the diminishing Ogallala Aquifer, making necessary the adoption of more efficient irrigation strategies. Decision Support System for Agrotechnology Transfer (DSSAT) is a process-based model that uses meteorological, soil, and crop management data to predict crop growth, development, and yield. A wellevaluated DSSAT model is useful for simulation of efficient crop and irrigation management strategies. This study details the evaluation of CROPGRO-Cotton module in the DSSAT model based on measured in-season biomass and canopy height, and crop yield data from a field study as well as the use of the evaluated model for determining the best irrigation strategy for cotton (Gossypium hirsutum L. var. hirsutum) in terms of crop yield and irrigation water use efficiency. Irrigation simulation experiments were conducted over a testing range for four separate irrigation scheduling strategies-Time Temperature Threshold (TTT)-5.5 h, TTT-7.5 h, Daily Irrigation (DI), and percent ET replacement-to determine the most efficient irrigation strategy that results in maximum yield with minimum irrigation water input. The DSSAT CROPGRO-Cotton model demonstrated potential to simulate the effects of various irrigation strategies on cotton yield and water use efficiency. The 12 mm, 7.5 h TTT strategy was found to be the best strategy to achieve a maximized yield with the greatest irrigation water use efficiency, with a modelled yield of 5887 kg ha −1 using 195 mm of irrigation throughout the season.
Turkish Journal of Agriculture - Food Science and Technology
This study was conducted in the Aegean region conditions of Turkey in 2020. It was carried out on May-505, a local cotton variety. The study examined the variation of seed yield, water use efficiency (WUE), and irrigation water use efficiency (IWUE) of cotton with different irrigation programs and water levels. The field trial, which was designed as two factors and three replications, was designed according to the randomized complete block trial design. Four different irrigation levels (IL) (100%, 67%, 33%, and 0%) and two different irrigation scheduling approaches (gravimetric and pan evaporation) were investigated in the study. Seasonal water use values in treatments varied between 215 (0%) and 746 (100% - Pan evaporation approach) mm during the production period. The average yield values obtained with irrigation levels, which have essential effects on cotton seed yield, are listed as follows; 2057 kg ha-1 (IL-0%), 3471 kg ha-1 (IL-33%), 3771 kg ha-1 (IL-67%), and 5083 kg ha-1 (IL...
Sensor-based Irrigation of Maize and Soybean in East-Central Nebraska under a Sub-Humid Climate
2021 ASABE Annual International Virtual Meeting, July 12-16, 2021, 2021
The ever increasing pressure on the water resources in Nebraska and other irrigated agricultural areas require innovations and solutions for the governance of water allocation. This study proposes the use of sensor-based method for irrigation which has the potential to improve irrigation water use efficiency (IWUE). Practical methods and algorithms for creating irrigation prescriptions have become vital for the adoption of precision irrigation. A decision support system (DSS) for uniform irrigation was evaluated during 2020 growing season in a sub-humid region. The DSS was managed using soil water and plant feedback. In field practice, a sensor node station comprising of soil water content sensors and infrared thermometer (IRT) was installed in maize and soybean. Root zone water depletion (Drw), and crop water stress index (CWSI) served as the inputs for soil water, and plant feedback, respectively. The timing and depth of irrigation was determined using the DSS. The results of the sensor-based DSS treatment were compared to conventional treatment (managed by a crop consultant) and rainfed (no-irrigation) treatment. Test results for maize and soybean indicated that there was no significant difference in crop yield between sensor-based and conventional treatments. However, the sensor-based DSS treatment witnessed higher IWUE for both maize and soybean. The observed yield for rainfed treatment was significantly lower than the irrigated treatments in maize and soybean. There is a great potential for the use of this DSS system for uniform irrigation in humid and sub-humid regions and future studies are required for the adoption of this technology.
Journal of Cotton Research
Background This study addressed the potential of combining a high biomass rye winter cover crop with predawn leaf water potential (ΨPD) irrigation thresholds to increase agricultural water use efficiency (WUE) in cotton. To this end, a study was conducted near Tifton, Georgia under a manually-controlled, variable-rate lateral irrigation system using a Scholander pressure chamber approach to measure leaf water potential and impose varying irrigation scheduling treatments during the growing season. ΨPD thresholds were − 0.4 MPa (T1), − 0.5 MPa (T2), and − 0.7 MPa (T3). A winter rye cover crop or conventional tillage were utilized for T1-T3 as well. Results Reductions in irrigation of up to 10% were noted in this study for the driest threshold (− 0.7 MPa) with no reduction in lint yield relative to the − 0.4 MPa and − 0.5 MPa thresholds. Drier conditions during flowering (2014) limited plant growth and node production, hastened cutout, and decreased yield and WUE relative to 2015. Conc...
Advances in Agriculture
In Sub-Saharan Africa, where most irrigation systems are manually operated, water allocation and irrigation scheduling are often based on uniform application irrespective of crop needs and growth stages, which results in nonoptimal water use. Recently, a lot of research has been carried out to improve irrigation water use efficiency through automation by employing wireless sensor-based monitoring systems. Further to the improvement of water use efficiency and yield, while reducing costs, a field trial was carried out at a farm in Harare, Zimbabwe, during the 2016, 2017, and 2018 winter seasons to test whether a new approach to the automated irrigation systems, one based on IoT and wirelessly connected soil sensors (called hereafter as WCSS), improves water use efficiency without reducing yield. WCSS method was compared with three widely used conventional irrigation methods, that is, manual scheduling, tensiometer-based scheduling, and weather-based scheduling. Impacts on water savin...
Identifying barriers to adoption of irrigation scheduling tools in Rio Grande Basin, 2021
Irrigated agriculture in the Rio Grande region faces water management related challenges due to climate variability and rise in non-agricultural water demand. Scientific irrigation scheduling (SIS) tools allow growers to optimize the water use and conserve water by making informed decisions. Nevertheless, multiple technological and economic barriers could slow down the adoption of these technologies. This study investigates the barriers to adoption of SIS methods in the U.S. part of Rio Grande basin by getting irrigators' perspective and outlines the factors that influence adoption. Multiple adoption barriers are listed, and the most important ones are lack of access to weather data, uncertainty about future water availability, cost effectiveness of technologies, reliability of weather data, lack of availability of irrigation scheduling tools, and risk of reduced yield. Factors that influence the growers' decision to adopt SIS are also explored, which are quality of land, yield, water use efficiency, and water availability for future generations. Age, education, and years involved in agriculture may also govern the knowledge and adoption of SIS methods. The results of this study provide guidance to policy makers and extension experts to strengthen water conservation efforts in Rio Grande basin and other comparable regions in the world.