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1. Simulating Soybean–Rice Rotation and Irrigation Strategies in Arkansas, USA Using APEX

   https://doi.org/10.3390/su12176822  

   Carroll, Sam R.; Le, Kieu Ngoc; Moreno-García, Beatriz; Runkle, Benjamin R. (September 2020, Sustainability)

   null (Ed.)

   With population growth and resource depletion, maximizing the efficiency of soybean (Glycine max [L.] Merr.) and rice (Oryza sativa L.) cropping systems is urgently needed. The goal of this study was to shed light on precise irrigation amounts and optimal agronomic practices via simulating rice–rice and soybean–rice crop rotations in the Agricultural Policy/Environmental eXtender (APEX) model. The APEX model was calibrated using observations from five fields under soybean–rice rotation in Arkansas from 2017 to 2019 and remote sensing leaf area index (LAI) values to assess modeled vegetation growth. Different irrigation practices were assessed, including conventional flooding (CVF), known as cascade, multiple inlet rice irrigation with polypipe (MIRI), and furrow irrigation (FIR). The amount of water used differed between fields, following each field’s measured or estimated input. Moreover, fields were managed with either continuous flooding (CF) or alternate wetting and drying (AWD) irrigation. Two 20-year scenarios were simulated to test yield changes: (1) between rice–rice and soybean–rice rotation and (2) under reduced irrigation amounts. After calibration with crop yield and LAI, the modeled LAI correlated to the observations with R2 values greater than 0.66, and the percent bias (PBIAS) values were within 32%. The PBIAS and percent difference for modeled versus observed yield were within 2.5% for rice and 15% for soybean. Contrary to expectation, the rice–rice and soybean–rice rotation yields were not statistically significant. The results of the reduced irrigation scenario differed by field, but reducing irrigation beyond 20% from the original amount input by the farmers significantly reduced yields in all fields, except for one field that was over-irrigated. 

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2. Estimating crop coefficients from canopy cover and height for a drip-irrigated young almond orchard: assessment using a two-source energy balance model

   https://doi.org/10.1007/s00271-024-00968-w  

   Montoya, F; Sánchez, J M; González-Piqueras, J; López-Urrea, R (November 2024, Irrigation Science)

   The aim of this study was to estimate standard crop coefficients of surface and sub-surface drip-irrigated young almond trees under non-limiting soil water content conditions, based on measurements of the fraction of ground covered by the canopy (fc) and tree height (h) (A&P approach proposed by Allen and Pereira (2009)) and to improve the transferability of them to the productive sector once weather data (i.e. maximum and minimum air temperature (Tmax and Tmin, respectively), as well as dew point temperature (Tdew)) were adjusted to the reference conditions. A 4 year field experiment was carried out in a ~ 12.5 ha commercial young almond (Prunus dulcis (Mill.) D.A. Webb) orchard located in Hellín, (SE Spain). 'Penta' almond trees, grafted onto the GF-677 rootstock, were planted in 2018. Field measurements of fc and h were performed over four consecutive growing seasons from 2019 to 2022. In parallel, ETo computed by the nearest meteorological station, located at a non-reference weather site, was reduced around 6% after bringing weather data closer to the reference conditions, while actual crop evapotranspiration and its components (actual tree transpiration and soil evaporation) were estimated in each irrigation system through the so-called simplified two-source energy balance (STSEB) model in order to be used as a quality assessment of the A&P approach. The ratio between the former estimations and the ETo allowed to compute STSEB-based crop coefficients. No significant differences in effective canopy cover (fc eff) nor h were observed between the two irrigation systems, and thus the estimated Kcb values were the same for both drip-irrigation systems. fc eff values during mid-season stage ranged between 0.15 in 2019 and 0.62 in 2022, whereas average h values for this stage ranged between 2.36 and 3.80 m in 2019 and 2022, respectively. These values of fc eff and h resulted in average mid-season basal crop coefficients (Kcb-mid) of 0.28 in 2019, 0.39 in 2020, 0.61 in 2021 and 1.02 in 2022. Soil evaporation estimates through STSEB model were significantly different between the two irrigation systems, leading to differences in Ke being around 16% higher for DI than SDI. Moreover, the intra-annual Kc values moved in the same range for the initial, mid- and end-season crop growth stages, varying from 0.30 in 2019 to 1.01 in 2022, computed under standard conditions. Finally, the A&P approach was shown to be an especially interesting method for estimating Kcb values in almond fruit trees, being useful for refining Kcb and/or Kc for conditions of plant spacing, size and density that may differ from standard values. In this way, irrigation scheduling can be optimized regarding the almond tree architecture (i.e. fc eff and h), allowing to manage properly irrigation water to meet the tree water demands. 

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3. Remote Sensing and Aerodynamic Temperature-Based Energy Balance Models to Estimate Crop Evapotranspiration Rates

   https://doi.org/10.5539/jas.v15n4p15  

   Chávez, José L. (March 2023, Journal of Agricultural Science)

   Different methods exist to measure or estimate actual crop evapotranspiration (ETa). However, some methods require a large number of data input or strict field conditions. Remote sensing based ETa algorithms based on extreme thermal pixels (hot and cold) have limitations when required extreme pixels are not present in the acquired thermal infra-red imagery. In addition, satellite overpass frequency and spatial pixel resolution may be a limitation for some agricultural fields and micro-climates. Surface energy balance methods that use surface radiometric temperatures often fail to perform well under drought, limited irrigation, salt affected soils, or under sparse vegetation conditions. One option is to measure or estimate the crop/surface sensible heat flux through the aerodynamic temperature approach, then calculate the available energy and solve the energy balance for latent heat flux. Thus, this study presents different published algorithms that characterize the crop or field surface aerodynamic temperature and then applies them to different conditions for evaluation. Determining spatial ETa continuously has the potential to improve the irrigation water management decision making. The aerodynamic temperature approach was initially developed with good results as a function of surface radiometric temperature, air temperature, crop leaf area index, and wind speed or surface aerodynamic resistance. However, the inclusion of the crop fractional percent cover and of a new resistance term (turbulent-mixing row resistance) greatly improved the estimation of the sensible heat and latent heat fluxes, when evaluated with heat flux data derived from eddy covariance energy balance towers. Results also indicate that the aerodynamic method has transferability potential to different regions, crops, and irrigation methods than the conditions encountered in the method development. 

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4. Using Machine Learning to Integrate On-Farm Sensors and Agro-Meteorology Networks into Site-Specific Decision Support

   https://doi.org/10.13031/trans.13917  

   Kelley, Jason; McCauley, Dalyn; Alexander, G. Aaron; Gray, Wilton F.; Siegfried, Rylie; Oldroyd, Holly J. (January 2020, Transactions of the ASABE)

   null (Ed.)

   Highlights Machine learning can incorporate a variety of data from low-cost sensors and estimate actual ET by comparison with short-term, higher-cost measurements. On-farm weather monitoring can be leveraged to estimate site-specific crop-water requirements. Expanding spatial coverage of weather and actual ET through on-farm monitoring will facilitate localization and leverage publicly available weather data to guide irrigation decisions and improve irrigation water management. Abstract . One of the basic challenges to adopting science-based irrigation scheduling is providing reliable, site-specific estimates of actual crop water demand. While agro-meteorology networks cover most agricultural production areas in the U.S., widely spaced stations represent regionally specific, rather than site-specific, conditions. A variety of low to moderate cost commercial weather stations are available but do not provide directly useful information, such as actual evapotranspiration (ETa), or the ability to incorporate additional sensors. We demonstrate that machine learning methods can provide real-time, site-specific information about ETa and crop water demand using on-farm sensors and public weather information. Two years of field experiments were conducted at four irrigated field sites with crops including snap beans, alfalfa, and pasture. On-farm data were compared to publicly available data originating at nearby agro-meteorology network stations. The machine learning procedure can robustly estimate ETa using data from a few basic sensors, but the resulting estimate is sensitive to the range of conditions that are used as training data. The results demonstrate that machine learning can be used with affordable sensors and publicly available data to improve local estimates of crop water demand when high-quality measurements can be co-located for short periods of time. Supplementary sensors can also be integrated into a tailored monitoring plan to estimate crop stress and other operational considerations. Keywords: Agro-meteorology, Irrigation requirement, Machine learning, Site-specific Irrigation. 

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5. Increased Dependence on Irrigated Crop Production Across the CONUS (1945–2015)

   https://doi.org/10.3390/w11071458  

   Smidt, Samuel J.; Kendall, Anthony D.; Hyndman, David W. (July 2019, Water)

   Efficient irrigation technologies, which seem to promise reduced production costs and water consumption in heavily irrigated areas, may instead be driving increased irrigation use in areas that were not traditionally irrigated. As a result, the total dependence on supplemental irrigation for crop production and revenue is steadily increasing across the contiguous United States. Quantifying this dependence has been hampered by a lack of comprehensive irrigated and dryland yield and harvested area data outside of major irrigated regions, despite the importance and long history of irrigation applications in agriculture. This study used a linear regression model to disaggregate lumped agricultural statistics and estimate average irrigated and dryland yields at the state level for five major row crops: corn, cotton, hay, soybeans, and wheat. For 1945–2015, we quantified crop production, irrigation enhancement revenue, and irrigated and dryland areas in both intensively irrigated and marginally-dependent states, where both irrigated and dryland farming practices are implemented. In 2015, we found that irrigating just the five commodity crops enhanced revenue by ~$7 billion across all states with irrigation. In states with both irrigated and dryland practices, 23% of total produced area relied on irrigation, resulting in 7% more production than from dryland practices. There was a clear response to increasing biofuel demand, with the addition of more than 3.6 million ha of irrigated corn and soybeans in the last decade in marginally-dependent states. Since 1945, we estimate that yield enhancement due to irrigation has resulted in over $465 billion in increased revenue across the contiguous United States (CONUS). Example applications of this dataset include estimating historical water use, evaluating the effects of environmental policies, developing new resource management strategies, economic risk analyses, and developing tools for farmer decision making. 

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