Project Overview
Commodities
- Vegetables: beets, broccoli, cabbages
Practices
- Crop Production: irrigation, water management
Abstract:
Scientific irrigation scheduling (SIS) can increase water use efficiency and support on-farm water conservation efforts to achieve sustainability in irrigated agriculture. A recent study in the Rio Grande Basin (Berthold et al., 2021) revealed that one of the adoption barriers to SIS was the lack of availability of irrigation scheduling tools. We propose the use of freely available and low-cost sensing technologies for optimizing the use of irrigation water for vegetable farming. This approach includes remote sensing-based vegetation indices (VIs) and soil moisture sensors. VIs can help growers detect the water stress in their cropped area under multiple pixels, and by employing soil moisture sensors, farmers can have access to data showing the water availability in the root zone. The combination of VIs and soil moisture sensors would provide growers confidence regarding their irrigation scheduling decisions. Using two different techniques to decide about irrigation would also help overcome any potential uncertainties and limitations associated with the data and estimates. Such practices would develop growers’ capacity to follow a data-driven approach for effectively managing the irrigation water that can support and strengthen sustainable farming in the region. The proposed solution has been discussed with numerous vegetable farmers (including farmer cooperator) in the Lower Rio Grande region during extension meetings and field visits in the past. Along with the shift from field crops to vegetables, the vegetable farming community in the region is adopting drip irrigation and realizing the need for SIS for improving on-farm water management. The proposed solution would also establish the grounds for the adoption of SIS under increasing pressure on agricultural water supplies due to competition with the municipal sector. The soil moisture sensor proposed under this study is CropX that is a cloud-based sensor and does not require any manual data retrieval. CropX sensors are being used globally and have reasonable accuracy under various soil types. CropX’s web-based user dashboard (graphic user interface) and mobile app are user-friendly. Also, CropX has active partnerships with organizations such as NASA Harvest, PEPSICO, and Reinke Irrigation to promote soil moisture analytics in precision farming.
Performance indicators
Performance indicators for the proposed research will be irrigation water applied, yield, and water use efficiency (WUE). The yield data will be collected at the end of each growing season and county extension office staff will collect the data both from experimental and control plots. Data of irrigation water applied will be collected from the irrigation logbook maintained by the farmer (irrigation time for each drip-irrigated zone and flow meter readings are recorded) and it will also be tallied with the change in soil moisture level via the in-situ sensor data. WUE will be estimated following equation 1 as suggested by Howell (2001).
WUE = Yield / (Pe + I + SW) (1)
Where Pe is effective rainfall, I is irrigation applied and SW is soil water depletion from the root zone during the growing season. SW can be calculated using the soil moisture data.
Data security/data privacy
Soil moisture data is only available to the person with an access code provided by the sensor manufacturer. The access code will be shared with the farmer. Soil moisture data from the experimental farm will be included in the factsheet and will be added to the presentations during webinars. The data will be stored for the long-term in Texas A&M AgriLife cloud storage under the agency’s data storage policies and will be accessible to the public at request.
Project objectives:
Approach:
The study will be conducted for vegetables (e.g., broccoli, cabbage, beetroot, etc.) at Tenaza farms in the Lower Rio Grande Valley, Texas. Two drip-irrigated adjacent plots of 25 acres will be selected: one for the field trial and one will be the control plot. Same planting date, crop variety, seeding rate, and row spacing will be selected for both plots. A two-step approach will be followed to develop growers’ capacity for using a combination of two sensing technologies in irrigation-related decisions: data from in-situ soil moisture sensors and VIs. In the first growing season, two auto-calibration and cloud-powered soil moisture sensors will be installed in two locations representative of major soil types in the experimental plot. The grower will have access to an online dashboard of the sensors that show in-situ soil moisture along with precipitation and potential evapotranspiration (ETo) data from the nearest weather station (Figure 1). Grower will be walked through the dashboard and will be sent detailed advisories regarding irrigation on a weekly/bi-weekly basis based on soil moisture data. Furthermore, the grower will be introduced to VIs in the first growing season and will be provided with VI imagery of the farm once a month. Grower feedback will also be requested regarding the level of agreement with the recommendations related to irrigation timing.
After the end of the first growing season, a comparison of the VIs and soil moisture timelines will be shared with the grower. Also, grower will receive a report on crop water use efficiency for both experimental and control plots to understand the water saving potential and yield improvement potential of SIS. Possibilities of further improving the irrigation scheduling will be discussed based on the data from first year, and the grower’s experience with the soil moisture sensors will be inquired. From the beginning of the second year, the grower will start receiving weekly/bi-weekly recommendations for scheduling irrigation based on both soil moisture and VIs. Also, the grower will be taught about ways to analyze the two data sources and make decisions. Grower will also be asked for feedback on his experience and any possible issues related to the provided recommendations. This will allow the grower to combine the benefits of these two technologies to elevate ag water stewardship on the farm. Eventually, at the end of the cropping season, the grower will be updated regarding the overall performance of the irrigation practice at the experimental field.
Methods
CropX soil moisture sensor (CropX Ltd., Tel Aviv, Israel) is proposed for this study. There are three main reasons to select this sensor. First is its helical wing that ensures minimum root zone soil disruption and avoids preferential flow; second is its built-in data logger with telemetry and cloud-based data storage that allows remote data collection; and third is its ability to sense soil moisture from multiple depths. The accuracy of CropX soil moisture sensors is +/-2%. The sensor measures the soil moisture at every 150ms that are averaged for a 30-minute interval, and the information is stored to the cloud after every 12 hours. The measurement depths of the sensor are 8” (≈ 20 cm) and 18” (≈ 46 cm). CropX sensor mechanism works on the amplitude domain reflectometry at 100MHz and the moisture in soil is estimated based on the difference in amplitude of an electrical signal moving towards and backward from the soil due to change in impedance between the electrodes and soil. The sensor provides soil moisture data in volumetric water content (VWC) and in terms of water depth (mm) for the root zone within the soil profile. Furthermore, rainfall data and potential evapotranspiration (ETo) data from the nearest weather station is provided under the online dashboard (Figure 1).
Freely available Landsat-8 and Sentinel imagery will be obtained. The former imagery source has spatial resolution of 30 m and temporal resolution of 16 days, and the latter source has spatial resolution of 10 m and temporal resolution of 5 days (Skakun et al., 2017). The imagery will be processed to estimate the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI). These indices will be estimated following equation 2 and 3.
NDVI = (NIR - R) / (NIR + R) (2)
where NIR is near infrared band and R is the red band.
NDWI = (NIR - SWIR) / (NIR + SWIR) (3)
where NIR is near-infrared band and SWIR is short wave infrared band.
The combination of these reflectance bands in NDVI enhances the vegetation properties and photosynthetic activity in crop whereas NDWI allows for detection of the moisture content in the crop canopy. Values for both indices vary between -1.0 and 1.0, where high value shows green and dense vegetation, lower values show water-stressed plants, and values near or below zero represent low biomass and exposed soil (Gessesse & Melesse, 2019). NDVI is widely used for monitoring drought and NDWI has shown high correlations with leaf water content (Alvino et al., 2020). Therefore, these indices can be considered useful for strengthening irrigation related decision making as early water stress can be detected in crop (Katsoulas et al., 2016). The impact of past events can be analyzed both at temporal and spatial scale, and adjustments in the future irrigation applications can be suggested to growers based on the trends shown by indices. These indices will be shared with the grower every 5 to 16 days, depending on the availability of imagery and cloud cover. The irrigation requirement of the crop will be estimated using the FAO-56 approach (Allen et al., 1998) using equation 4.
ETc = ETo x Kc (4)
where ETc is the crop water requirement, ETo is the potential evapotranspiration, and Kc is the crop coefficient. ETo will be estimated using the data from the nearest weather station, and Kc for initial, middle, and late stages. Soil hydraulic properties (i.e., field capacity and wilting point) will be retrieved from a recently conducted analysis of multiple soil samples collected from the farm.Response to reviewers comments (previous submission)