More frequent and more intense heat waves and greater drought stress will occur in the future climate environment. Short-term extreme heat and drought stress often occur simultaneously after winter wheat anthesis, which has become the major constraint threatening future wheat yield. In this study, short-term heat, drought and their combination stress were applied to wheat plants after anthesis, and all wheat plants were restored to the outdoor normal temperature and full watering after stress treatment. The aim of the current study was to evaluate the role of nitrogen (N) in modulating the effects of post-anthesis short-term heat, drought and their combination stress on photosynthesis, N metabolism-related enzymes, the accumulation of N and protein and growth, as well as on the yield and water (WUE) and N use efficiency (NUE) of wheat after stress treatment. The results showed that compared with low N application (N1), medium application (N2) enhanced the activities of nitrate reductase (NR) and glutamine synthase (GS) in grains under post-anthesis heat and drought stress alone, which provided a basis for the accumulation of N and protein in grains at the later stage of growth. Under post-anthesis individual stresses, N2 or high application (N3) increased the leaf photosynthetic rate (A_n), PSII photochemical efficiency and instantaneous WUE compared with N1, whereas these parameters were usually significantly improved by N1 application under post-anthesis combined stress. The positive effect of increased A_n by N application on growth was well represented in a higher green leaf area, aboveground dry mass and plant height, and the variation in A_n can be explained more accurately by the N content per unit leaf area. Short-term heat, drought and combined stress after anthesis resulted in a pronounced decrease in yield by reducing grain number per spike and thousand kernel weight. The reduction in NUE under combined stress was higher than that under individual heat and drought stress. Compared with N1, N2 or N3 application significantly prevented the decrease in yield and NUE caused by post-anthesis heat and drought stress alone. However, N1 application was conducive to improving the productivity, WUE and NUE of wheat when exposed to post-anthesis combined stress. The current data indicated that under short-term individual heat and drought stress after anthesis, appropriately increasing N application effectively improved the growth and physiological activity of wheat compared with N1, alleviating the reduction in yield, WUE and NUE. However, under combined stress conditions, reducing N application (N1) may be a suitable strategy to compensate for the decrease in yield, WUE and NUE.

This study examines the use of leaf area index (LAI) to inform variable-rate irrigation (VRI) for irrigated alfalfa (Medicago sativa). LAI is useful for predicting zone-specific evapotranspiration (ET_c). One approach toward estimating LAI is to utilize the relationship between LAI and visible vegetation indices (VVIs) using unmanned aerial vehicle (UAV) imagery. This research has three objectives: (1) to measure and describe the within-field variation in LAI and canopy height for an irrigated alfalfa field, (2) to evaluate the relationships between the alfalfa LAI and various VVIs with and without field average canopy height, and (3) to use UAV images and field average canopy height to describe the within-field variation in LAI and the potential application to VRI. The study was conducted in 2021–2022 in Rexburg, Idaho. Over the course of the study, the measured LAI varied from 0.23 m² m⁻² to 11.28 m² m⁻² and canopy height varied from 6 cm to 65 cm. There was strong spatial clustering in the measured LAI but the spatial patterns were dynamic between dates. Among eleven VVIs evaluated, the four that combined green and red wavelengths but excluded blue wavelengths showed the most promise. For all VVIs, adding average canopy height to multiple linear regression improved LAI prediction. The regression model using the modified green–red vegetation index (MGRVI) and canopy height (R² = 0.93) was applied to describe the spatial variation in the LAI among VRI zones. There were significant (p < 0.05) but not practical differences (<15%) between pre-defined zones. UAV imagery coupled with field average canopy height can be a useful tool for predicting LAI in alfalfa.

Understanding spatial and temporal dynamics of soil water within fields is critical for effective variable rate irrigation (VRI) management. The objectives of this study were to develop VRI zones, manage irrigation rates within VRI zones, and examine temporal differences in soil volumetric water content (VWC) from irrigation events via soil sensors across zones. Five irrigation zones were delineated after two years (2016 and 2017) of yield and evapotranspiration (ET) data collection. Soil sensors were placed within each zone to give real time data of VWC values and assist in irrigation decisions within a 23 ha field of winter wheat (Triticum aestivum ‘UI Magic’) near Grace, Idaho, USA (2019). Cumulative irrigation rates among zones ranged from 236 to 298 mm. Although a statistical comparison could not be made, the irrigation rates were 0.6 to 21% less than an estimated uniform grower standard practice (GSP) irrigation approach. Based on soil sensor data, crop water stress was avoided with VRI management in all but Zone 3. Thus, this simple approach to VRI zone delineation and VWC monitoring has the potential to reduce irrigation, such as this study, on average by 12% and should be evaluated in other site-years to assess its viability.

Purpose: Zones for spatial irrigation management are often assumed to be static, dictated by non- or slowly-changing parameters, including elevation and slope, soil depth, texture and hydraulic properties, and other landscape properties. Sensing allows for the integration of static management zones (MZ) with a dynamic characterization of water status. Temporal remote sensing data, indicating crop status and, particularly, thermal imaging, indicating water status, suggests that, rather than static, dynamic re-zoning within crop seasons could be beneficial to precision water management. Methods: MZ delineation and precision irrigation for cotton were evaluated using a variable rate center pivot sprinkler system. The objectives were to evaluate precision management over time with both static and intra-seasonal dynamic zoning. Spatial distribution and irrigation decision-making were re-evaluated on a weekly basis. Irrigation followed commercial best management, based on target values of plant size (growth rate) in the early vegetative growth period and of plant water status (leaf water potential, LWP) in the reproductive and cotton boll production periods. Site-specific decision-making used drone-acquired normalized difference vegetation index as a proxy for crop height and growth rate and a thermal image-based crop water stress index as a proxy for LWP. Results and conclusion: Dynamic precision irrigation of cotton based on weekly decisions using the remote sensing products slightly decreased spatial variability while improving water productivity by 12% using static MZs and by 19% using dynamic MZs, compared to commercial uniform irrigation. The costs and benefits of implementing intra-seasonal dynamic zoning are discussed.

Irrigated turfgrass is a major crop in urban areas of the drought-stricken Western United States. A considerable proportion of irrigation water is wasted through the use of conventional sprinkler systems. While smart sprinkler systems have made progress in reducing temporal mis-applications, more research is needed to determine the most appropriate variables for accurately and cost-effectively determining spatial zones for irrigation application. This research uses data from ground and drone surveys of two large sports fields. Surveys were conducted pre-, within and towards the end of the irrigation season to determine spatial irrigation zones. Principal components analysis and k-means classification were used to develop zones using several variables individually and combined. The errors associated with uniform irrigation and different configurations of spatial zones are assessed to determine comparative improvements in irrigation efficiency afforded by spatial irrigation zones. A determination is also made as to whether the spatial zones can be temporally static or need to be re-determined periodically. Results suggest that zones based on spatial soil moisture surveys and simple observations of whether the grass felt wet or dry are better than those based on NDVI, other variables and several variables in combination. In addition, due to the temporal variations observed in spatial patterns, ideally zones should be re-evaluated periodically. However, a less labor-intensive solution is to determine temporally static zones based on patterns in soil moisture averaged from several surveys. Of particular importance are the spatial patterns observed prior to the start of the irrigation season as they reflect more temporally stable variation that relates to soil texture and topography rather than irrigation management.

Drought is a principal limiting factor in the production of agricultural crops; however, quinoa possesses certain adaptive and tolerance factors that make it a potentially valuable crop under drought-stress conditions. Within this context, the objective of the present study was to evaluate morphological and physiological changes in ten quinoa genotypes under three irrigation treatments: normal irrigation, drought-stress followed by recovery irrigation, and terminal drought stress. The experiments were conducted at the UNSA Experimental Farm in Majes, Arequipa, Peru. A series of morphological, physiological, and remote measurements were taken, including plant height, dry biomass, leaf area, stomatal density, relative water content, selection indices, chlorophyll content via SPAD, multispectral imaging, and reflectance measurements via spectroradiometry. The results indicated that there were numerous changes under the conditions of terminal drought stress; the yield variables of total dry biomass, leaf area, and plant height were reduced by 69.86%, 62.69%, and 27.16%, respectively; however, under drought stress with recovery irrigation, these changes were less pronounced with a reduction of 21.10%, 27.43%, and 17.87%, respectively, indicating that some genotypes are adapted or tolerant of both water-limiting conditions (Accession 50, Salcedo INIA and Accession 49). Remote sensing tools such as drones and spectroradiometry generated reliable, rapid, and precise data for monitoring stress and phenotyping quinoa and the optimum timing for collecting these data and predicting yield impacts was from 79–89 days after sowing (NDRE and CREDG r Pearson 0.85).