Research into precision irrigation management of alfalfa begins | University of Nevada, Reno

Agricultural Research Project Aims to Improve Water Management for Alfalfa Crop

A three-year research project led by the University of Nevada, Reno will develop open-source software to help farmers better manage water supply, especially for Nevada’s crucial crop of alfalfa. The project is funded by the United States Department of Agriculture’s National Institute of Food and Agriculture as a grant of its Engineering for Precision Crop and Water Management Program.

Collaborative Effort and Funding

The College of Agriculture, Biotechnology & Natural Resources received about $400,000 for its part in the $745,000 collaborative project with the University of California-Davis and the USDA’s Agricultural Research Service in Bushland, Texas. The three institutions are working on different crops as part of the grant: alfalfa for Reno, corn for Davis, and cotton for Bushland.

Research Objectives and Timeline

The University of Nevada, Reno portion of the research started in July and concludes in 2026. The University will develop precision irrigation management software, conducting field experiments to test and improve the software. The research is being conducted on an alfalfa field at the Valley Road Field Lab, which is part of the College’s Experiment Station unit.

Principal Investigator and Expertise

Alejandro Andrade-Rodriguez is an assistant professor in the University’s Department of Agriculture, Veterinary & Rangeland Sciences, and is the principal investigator of this project, “Partnership: Making the most of a limited water supply by improving a site-specific irrigation management decision support system.” Andrade-Rodriguez has extensive knowledge of irrigation hardware and software and has conducted research on irrigation management before as part of the Experiment Station unit.

More about Precision Irrigation

This latest research for Andrade-Rodriguez ties into his post-doctoral research in the 2010s at the USDA-Agricultural Research Service in Bushland, Texas. That is where he developed ARSPivot, decision support software for center pivot irrigation systems equipped for precision irrigation. Center pivots are the most common irrigation systems in the U.S., and they irrigate about a third of the acreage in Nevada, Andrade-Rodriguez said.

“Precision irrigation allows center pivot systems to apply variable amounts of water to a field, which can lead to water savings,” he said. “These systems allow water to be applied in the right amounts required by plants growing in different parts of a field.”

Andrade-Rodriguez said the objective of conventional center pivot irrigation systems is to have every part of a field receive the same amount of water.

“If the entirety of a field has similar characteristics, you don’t want to irrigate more here or there, because that will cause lower yields in some portions of the field and higher yields in others,” he explained. “This is why you want to irrigate the same across a uniform field. But, the reality is that you may have different soil types in a field, or there may be areas with higher or lower elevations. Applying the same irrigation amounts to such a field will result in poor water management.”

Providing Help for Farming Decisions

Through precision irrigation, growers can alter the amounts of water applied to different parts of the field, to make a more efficient use of irrigation water. Yet, adoption of these systems has been slower than expected since they became commercially available in 2004, in part because there is a lack of decision support systems capable of estimating the water requirements for a field’s different sections.

ARSPivot was developed with this purpose in mind. The software automatically generates precision irrigation maps for center pivot systems using weather, soil moisture, and plant sensing systems. ARSPivot uses measurements from crop canopy temperature sensors collected at different parts of a field to estimate water stress and water need.

“For example, when canopy temperatures are higher in a certain part of a field, more irrigation is needed there,” he said.

This current phase of Andrade-Rodriguez’s work with ARSPivot is centered on expanding the software capabilities to make it compatible with different manufacturers of center pivot systems. The current version of ARSPivot only supports center pivots from one manufacturer.

The new version of the software will also be able to operate on linear move irrigation systems in addition to center pivots. Both linear move and center pivots are self-propelled sprinkler irrigation systems used to water rectangular and circular fields, respectively.

Although center pivots are considerably more common than linear move systems, supporting linear move systems is important for agricultural researchers, since these systems irrigate the rectangular shapes of agricultural fields common in experiment stations. The new version of the software will be tested at the Valley Road Field Lab using a linear move system.

The new version of ARSPivot will also incorporate an interface to a crop water use and yield model, with the objective of allowing the software to analyze many different potential irrigation management decisions (such as when to irrigate, and how much to irrigate in different parts of a field). This effort will be led by Isaya Kisekka with the University of California-Davis, who is a co-principal investigator of the project.

The new version of ARSPivot will be released as open-source software that Andrade-Rodriguez expects to provide irrigation researchers with a much-needed open platform that facilitates the generation, testing, and implementation of innovative precision irrigation management strategies.

The Importance of Alfalfa

Andrade-Rodriguez said precision irrigation using ARSPivot is being used for alfalfa for the first time as part of his research. He added that the sensor-based decision support system has been tested with corn, soybeans, cotton, grain sorghum, and potatoes in several states, and there have been good results from it.

“In general, there have not been significant differences in the yield obtained per unit

SDGs, Targets, and Indicators

1. Sustainable Development Goals (SDGs), Targets, and Indicators

1. SDG 2: Zero Hunger
• Target 2.4: By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems, that strengthen capacity for adaptation to climate change, extreme weather, drought, flooding, and other disasters, and that progressively improve land and soil quality.
• Indicator 2.4.1: Proportion of agricultural area under productive and sustainable agriculture
• Indicator 2.4.2: Proportion of total agricultural population with ownership or secure rights over agricultural land, by sex; and Indicator 2.4.3: Average income of small-scale food producers, by sex and indigenous status
2. SDG 6: Clean Water and Sanitation
• Target 6.4: By 2030, substantially increase water-use efficiency across all sectors and ensure sustainable withdrawals and supply of freshwater to address water scarcity and substantially reduce the number of people suffering from water scarcity.
• Indicator 6.4.1: Change in water-use efficiency over time
• Indicator 6.4.2: Level of water stress: freshwater withdrawal as a proportion of available freshwater resources

2. Specific Targets

Based on the content of the article, the following specific targets can be identified:

– Target 2.4: By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems, that strengthen capacity for adaptation to climate change, extreme weather, drought, flooding, and other disasters, and that progressively improve land and soil quality.

– Target 6.4: By 2030, substantially increase water-use efficiency across all sectors and ensure sustainable withdrawals and supply of freshwater to address water scarcity and substantially reduce the number of people suffering from water scarcity.

3. Indicators

The article mentions or implies the following indicators that can be used to measure progress towards the identified targets:

– Indicator 2.4.1: Proportion of agricultural area under productive and sustainable agriculture.

– Indicator 2.4.2: Proportion of total agricultural population with ownership or secure rights over agricultural land, by sex; and Indicator 2.4.3: Average income of small-scale food producers, by sex and indigenous status.

– Indicator 6.4.1: Change in water-use efficiency over time.

– Indicator 6.4.2: Level of water stress: freshwater withdrawal as a proportion of available freshwater resources.

These indicators can be used to assess the progress made in implementing sustainable agricultural practices and improving water-use efficiency in the context of the specific targets mentioned in the article.

SDGs, Targets, and Indicators Table

Behold! This splendid article springs forth from the wellspring of knowledge, shaped by a wondrous proprietary AI technology that delved into a vast ocean of data, illuminating the path towards the Sustainable Development Goals. Remember that all rights are reserved by SDG Investors LLC, empowering us to champion progress together.

Source: unr.edu

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