Assessing Within-Field Variation in Alfalfa Leaf Area Index Using UAV Visible Vegetation Indices

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 (ETc). 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 \u3c 0.05) but not practical differences

Spatial analysis of soil moisture and turfgrass health to determine zones for spatially variable irrigation management

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

Effect Of Gender On T-Cell Proliferative Responses To Myelin Proteolipid Protein Antigens In Patients With Multiple Sclerosis And Controls

Multiple sclerosis (MS) is an inflammatory demyelinating disorder of the central nervous system. Gender influences both susceptibility to MS, with the disease being more common in women, and the clinical course of disease, with an increased proportion of males developing the primary progressive form of the disease. The basis for these differences may include genetic and immunological factors, and the immunological differences between men and women may be influenced by the effects of the sex hormones. Over several years we have collected blood from MS patients and controls, and measured T-cell responses to myelin proteolipid protein (PLP) and myelin basic protein (MBP) and have shown increased responses to PLP in MS patients compared to healthy controls and patients with other neurological diseases. In the present study we analyzed data from over 500 individuals, to determine whether there are differences between males and females in their responses to PLP and MBP. We found that there was higher frequency of increased T-cell reactivity to immunodominant PLP peptides in women than in men, particularly in non-MS individuals. We suggest that this may be relevant to the higher prevalence of MS in women

Automated analysis of snowmelt from Sentinel-2 imagery to determine variable rate irrigation zones in the American Mountain West

Variable rate irrigation (VRI) is used to save water whilst maintaining crop yields in semiarid regions. A key problem is to be able to inexpensively determine spatial patterns in soil moisture so that VRI zones can be defined. In Southern Idaho, USA, the annual precipitation is low and most fall as winter snow. This research investigates whether snow melt patterns measured using freely available time-series Sentinel 2 imagery from Google Earth Engine can define useful VRI zones for two arable fields (Grace and Rexburg). The normalized difference snow index (NDSI) was computed for each 10 m pixel with snow for all winter images of the fields for 2018–2022. NDSI values were ranked within each image and average ranks were calculated for each month and over several years. The patterns of March NDSI were most similar to patterns in yield and soil moisture observed in previous years. Zones were determined using K-means classification of the mean ranks of March NDSI. Kruskal Wallis H tests showed consistent and significant differences between zones for key soil, plant, and topographic variables. For the Grace site, differences between zones were more consistent in their order of magnitude than VRI zones which were calculated using a labor-intensive method. For the Rexburg site, zones were shown to be better when based on snowmelt data from March 2018 to 2022 rather than just March 2019. It is important to base zones on several years of data because in some years there was no snow observed in the Grace field in March. In locations where the majority of soil moisture comes from snowmelt, basing VRI zones on several years of snowmelt patterns in March is a useful and inexpensive tool for deriving meaningful VRI zones. The code used to automatically extract suitable sentinel images and calculate the NDSI is included so that practitioners can use this approach in other locations