An official website of the United States government
e. Precision agriculture accounts for within-field variability for targeted treatment rather than uniform treatment of an entire field. It is built on agricultural mechanization and state-of-the-art technologies of geographical information systems (GIS), global positioning systems (GPS) and remote sensing, and is used to monitor soil, crop growth, weed infestation, insects, diseases, and water status in farm fields to provide data and information to guide agricultural management practices. Precision agriculture began with mapping of crop fields at different scales to support agricultural planning and decision making. With the development of variable-rate technology, precision agriculture focuses more on tactical actions in controlling variable-rate seeding, fertilizer and pesticide application, and irrigation in real-time or within the crop season instead of mapping a field in one crop season to make decisions for the next crop season. With the development of aerial variable-rate systems, low-altitude airborne systems can provide high-resolution data for prescription variable-rate.
more »
« less
WaterSmart-GIS: A Web Application of a Data Assimilation Model to Support Irrigation Research and Decision Making
Irrigation is the primary consumer of freshwater by humans and accounts for over 70% of all annual water use. However, due to the shortage of open critical information in agriculture such as soil, precipitation, and crop status, farmers heavily rely on empirical knowledge to schedule irrigation and tend to excessive irrigation to ensure crop yields. This paper presents WaterSmart-GIS, a web-based geographic information system (GIS), to collect and disseminate near-real-time information critical for irrigation scheduling, such as soil moisture, evapotranspiration, precipitation, and humidity, to stakeholders. The disseminated datasets include both numerical model results of reanalysis and forecasting from HRLDAS (High-Resolution Land Data Assimilation System), and the remote sensing datasets from NASA SMAP (Soil Moisture Active Passive) and MODIS (Moderate-Resolution Imaging Spectroradiometer). The system aims to quickly and easily create a smart, customized irrigation scheduler for individual fields to relieve the burden on farmers and to significantly reduce wasted water, energy, and equipment due to excessive irrigation. The system is prototyped here with an application in Nebraska, demonstrating its ability to collect and deliver information to end-users via the web application, which provides online analytic functionality such as point-based query, spatial statistics, and timeseries query. Systems such as this will play a critical role in the next few decades to sustain agriculture, which faces great challenges from climate change and increased natural disasters.
more »
« less
- Award ID(s):
- 1739705
- PAR ID:
- 10376782
- Date Published:
- Journal Name:
- ISPRS International Journal of Geo-Information
- Volume:
- 11
- Issue:
- 5
- ISSN:
- 2220-9964
- Page Range / eLocation ID:
- 271
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Given the increasing prevalence of droughts, unpredictable rainfall patterns, and limited access to dependable water sources in the United States and worldwide, it has become crucial to implement effective irrigation scheduling strategies. Irrigation is triggered when some variables, such as soil moisture or accumulated water deficit, exceed a given threshold in the most common approaches applied in irrigation scheduling. A High-Resolution Land Data Assimilation System (HRLDAS) was used in this study to generate timely and accurate soil moisture and evapotranspiration (ET) data for irrigation management. By integrating HRLDAS products and the crop growth model (AquaCrop), an automated data-driven irrigation scheduling approach was developed and evaluated. For HRLDAS ET and soil moisture, the ET-water balance (ET-WB)-based method and soil-moisture-based method were applied accordingly. The ET-WB-based method showed a 10.6~33.5% water-saving result in dry and set seasons, whereas the soil moisture-based method saved 7.2~37.4% of irrigation water in different weather conditions. Both of these methods demonstrated good results in saving water (with a varying range of 10~40%) without harming crop yield. The optimized thresholds in the two approaches were partially consistent with the default values from the Food and Agriculture Organization and showed a similar trend in the growing season. Furthermore, the forecasted rainfall was integrated into this model to see its water-saving effect. The results showed that an additional 10% of irrigation water, which is 20~50%, can be saved without harming the crop yield. This study automated the data-driven approach for irrigation scheduling by taking advantage of HRLDAS products, which can be generated in a near-real-time manner. The results indicated the great potential of this automated approach for saving water and irrigation decision making.more » « less
-
Crop growth depends on the root-zone soil moisture (RZSM) (~top 1m). Accurate estimation of RZSM is vital to optimize irrigation management for saving water and energy while sustaining crop yield. The High-Resolution Land Assimilation System (HRLDAS) from NCAR can generate RZSM at field scales for irrigation management. The soil moisture data from various agriculture sites in the AmeriFlux network, U.S. Climate Reference Network (USCRN), and Soil Climate Analysis Network (SCAN) are used to verify the soil moisture products generated by HRLDAS. Although the HRLDAS products is not location specific and could be applied nationwide, this study will focus on Nebraska for evaluation, validation, and further calibration. We also compared NASA’s SMAP surface soil moisture products to HRLDAS surface layer soil moisture. Since the accuracy of the SMAP product is known, this comparison directly validates the HRLDAS surface soil moisture product and indirectly validate its RZSM products. Results from these two validation methods show a good accuracy of HRLDAS soil moisture products. The conspicuous differences between HRLDAS and SMAP products indicate that HRLDAS omits the irrigation activities as its simulation is based on weather variables and energy balance. It’s hard for HRLDAS to consider and include the irrigation actions in its results, while as SMAP products remotely sense the soil moisture as it is, the changes caused by irrigation are clearly reflected. Therefore, a simple calibration is applied to the HRLDAS products by including irrigation amount as its variables.more » « less
-
Abstract Correctly calculating the timing and amount of crop irrigation is crucial for capturing irrigation effects on surface water and energy budgets and land‐atmosphere interactions. This study incorporated a dynamic irrigation scheme into the Noah with multiparameterization land surface model and investigated three methods of determining crop growing season length by agriculture management data. The irrigation scheme was assessed at field scales using observations from two contrasting (irrigated and rainfed) AmeriFlux sites near Mead, Nebraska. Results show that crop‐specific growing‐season length helped capture the first application timing and total irrigation amount, especially for soybeans. With a calibrated soil‐moisture triggering threshold (IRR_CRI), using planting and harvesting dates alone could reasonably predict the first application for maize. For soybeans, additional constraints on growing season were required to correct an early bias in the first modeled application. Realistic leaf area index input was essential for identifying the leaf area index‐based growing season. When transitioning from field to regional scales, the county‐level calibrated IRR_CRI helped mitigate overestimated (underestimated) total irrigation amount in southeastern Nebraska (lower Mississippi River Basin). In these two heavily irrigated regions, irrigation produced a cooling effect of 0.8–1.4 K, a moistening effect of 1.2–2.4 g/kg, a reduction in sensible heat flux by 60–105 W/m2, and an increase in latent heat flux by 75–120 W/m2. Most of irrigation water was used to increase soil moisture and evaporation, rather than runoff. Lacking regional‐scale irrigation timing and crop‐specific parameters makes transferring the evaluation and parameter‐constraint methods from field to regional scales difficult.more » « less
-
Abstract Irrigation is an important adaptation to reduce crop yield loss due to water stress from both soil water deficit (low soil moisture) and atmospheric aridity (high vapor pressure deficit, VPD). Traditionally, irrigation has primarily focused on soil water deficit. Observational evidence demonstrates that stomatal conductance is co-regulated by soil moisture and VPD from water supply and demand aspects. Here we use a validated hydraulically-driven ecosystem model to reproduce the co-regulation pattern. Specifically, we propose a plant-centric irrigation scheme considering water supply-demand dynamics (SDD), and compare it with soil-moisture-based irrigation scheme (management allowable depletion, MAD) for continuous maize cropping systems in Nebraska, United States. We find that, under current climate conditions, the plant-centric SDD irrigation scheme combining soil moisture and VPD, could significantly reduce irrigation water use (−24.0%) while maintaining crop yields, and increase economic profits (+11.2%) and irrigation water productivity (+25.2%) compared with MAD, thus SDD could significantly improve water sustainability.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/ijgi11050271
