<p dir="ltr"><a href="" target="_blank">Watermelon [<i>Citrullus lanatus </i>(Thunb.) Matsum. & Nakai] is one of the world's top three most consumed fruits.</a> Indiana cultivates approximately 7,000 acres of watermelons every year, ranking 6^th in the nation. More than 70% of this production is concentrated in and around Knox County, making Southern Indiana a key region for watermelon production in the States. Despite its significance, watermelon production faces many challenges, including erratic rainfall patterns exacerbated by climate change. Enhanced irrigation management has emerged as a critical strategy in mitigating negative environmental effects and in optimizing fertilizer applications.</p><p dir="ltr">Currently, Southern Indiana farmers have incorporated different irrigation and fertilization practices into watermelon production, yet the effects on production outcomes remain poorly understood. To bridge this gap in knowledge, this study aims to explore the effects of existing practices on watermelon yield and develop irrigation guidelines for optimal production in the region. The experiment was conducted at Southwest Purdue Agricultural Center, Vincennes, Indiana, in 2022 and 2023. Four treatments were applied: High Irrigation, Low Irrigation, No Irrigation, and Fertigation. Fertigation treatment received the same water application as the High Irrigation treatment. Fertilizers were applied pre-plant in the High, Low, and No irrigation treatments, while frequent fertigation was applied to the Fertigation Treatment. Soil moisture sensors measuring volumetric water content were used for irrigation decisions. In 2022, the irrigation thresholds were set at 15% water depletion at 1-ft depth for High Irrigation and Fertigation treatment, and 2-ft depth for Low Irrigation. In 2023, the irrigation threshold for Low Irrigation was adjusted to 40% water depletion at 1-ft depth.</p><p dir="ltr">While soil moisture levels in the bed at the different depths varied notably among treatments, no significant differences in yield by weight were observed. The minimal impact of irrigation on watermelon yield suggests that rainfall provides sufficient water, preventing yield-reducing stress. However, the Fertigation and High Irrigation treatments yielded more fruit than the Low Irrigation and No Irrigation treatments. The dry periods in both years coincided with the watermelon fruit setting stages that may have contributed to the lower fruit set in the Low Irrigation and No Irrigation treatments. Fertigation showed a higher early yield than the other treatments in 2022. Analysis of soil and tissue nitrogen levels indicated that sole nitrogen application before planting could result in excessive soil nitrogen levels during vegetative growth. This excess nitrogen might delay flowering and harvest. This project offers insights into enhancing irrigation and fertilization practices for watermelon production in southern Indiana.</p>

Forests account for a large portion of global carbon storage. Almost half of this carbon is stored underground. Roots, especially fine-roots (diameter ≤ 2 mm) are an important part of soil carbon, and play a number of ecological roles. Yet, the below-ground parts of trees are still poorly understood. The aim of this report was to understand environmental controls over plants traits. I tested if plants shift their root traits towards a resource conservative strategy when environmental conditions are challenging (dry and cold). Furthermore, the contribution of different fine-root diameters to total root length (TRL), surface area (SA) and root volume (VO) was assessed. Root traits such as TRL, SA, VO, fine-root diameter, specific root length (SRL) and root tissue density (RTD) were quantified. The root samples are taken from 11 old growth Swedish forests across a climate gradient in three different soil moisture categories per forest (dry, intermediate, wet). Fine-roots were scanned, and analysed with the RhizoVision Explorer Software (Version 2.0.3). Uni-factorial ANOVAs, combined with Tukey’s posthoc test, or linear regressions were conducted, and the results revealed no significant effects of the summer air temperature on root traits. However, higher soil moisture was found to decrease RTD, but increase fine-root diameters significantly. These findings did not confirm the plants aim on building resource conservative root traits in challenging conditions. Instead, they highlighted variability among and within forests suggest that multiple factors influence root morphology and function. This study contributes to our understanding of how fine-roots respond to environmental conditions.

This investigation has for its aim the confirmation of the correlation between soil moisture and the microflora of soils, and a lesser relation between the soil temperature and the microflora. The soils used in this study are typical clay soils of Denton County, Texas. The samples were taken from virgin pasture lands and analyses made monthly or semimonthly. The wide seasonal variation of temperature and rainfall afford a good opportunity for this study.

Hitherto, most research conducted to monitor agricultural drought on the African continent has focused only on meteorological aspects, with less attention paid to soil moisture, which describes agricultural drought. Satellite missions dedicated to soil moisture monitoring must be used with caution across various scales. The rainfed sector of Sudan takes great importance due to it is high potential to support national food security. El Gedaref state is significant in Sudan given its potentiality of the agricultural sector under a mechanized system, where crop cultivation supports livelihood sources for about 80% of its population and households, directly through agricultural production and indirectly through labor workforce. The state is an essential rainfed region for sorghum production, located within Sudan's Central Clay Plain (CCP). Enhancing soil moisture estimation is key to boosting the understanding of agricultural drought in the farming lands of Sudan. Soil moisture measuring stations/sensors networks do not exist in the El Gedaref agricultural rainfed sector. The literature shows a significant gap in whether soil moisture is sufficient to meet the estimated water demands of cultivation or the start of the growing season. The purpose of this study is to focus principally on agricultural drought. The soil moisture data retrieved from the Soil Moisture Active Passive (SMAP) mission launched by NASA in 2015 were compared against in situ data measurements over the agricultural lands. In situ points (at 5 cm, 10 cm, and 20 cm depths) corresponding to 9×9 km SMAP pixel foot-print are rescaled to conduct a point-to-pixel evaluation of SMAP product over two locations, namely Samsam and Kilo-6, during the rainy season 2018. Four errors were measured; Root Mean Squared Error (RMSE), Mean Bias Error (MBE), unbiased RMSE (ubRMSE), Mean Absolute Bias Error (MABE), and the coefficient of determination R2. SMAP improve (significantly at the 5% level for SM). The results indicated that the SMAP product meets its soil moisture accuracy requirement at the top 5 cm and in the root zone (10 and 20 cm) depths at Samsam and Kilo-6. SMAP demonstrates higher performance indicated by the high R2 (0.96, 0.88, and 0.97) and (0.85, 0.94, and 0.94) over Samsam and Kilo-6, respectively, and met its accuracy targeted by SMAP retrieval domain at ubRMSE 0.04 m3m-3 or better in all locations, and most minor errors (MBE, MABE, and RMSE). The possibility of using SMAP products was discussed to measure agricultural drought and its impacts on crop growth during various growth stages in both locations and over the CCP entirely. The croplands of El Gedaref are located within the tropical savanna (AW, categorization following the Köppen climate classification), warm semi-arid climate (BSh), and warm desert climate (BWh). The areas of interest are predominantly rainfed agricultural lands, vulnerable to climate change and variability. The Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), SMAP at the top surface of the soil and the root zone, and Soil Water Deficit Index (SWDI) derived from SMAP were analyzed against the Normalized Difference Vegetation Index (NDVI). The results indicate that the NDVI val-ues disagree with rainfall patterns at the dekadal scale. At all isohyets, SWDI in the root zone shows a reliable and expected response of capturing seasonal dynamics concerning the vegetation index (NDVI) over warm desert climates during 2015, 2016, 2017, 2018, and 2019, respectively. It is concluded that SWDI can be used to monitor agricultural drought better than rainfall data and SMAP data because it deals directly with the available water content of the crops. SWDI monitoring agricultural drought is a promising method for early drought warning, which can be used for agricultural drought risk management in semi-arid climates. The comparison between sorghum yield and the spatially distributed water balance model was assessed according to the length of the growing period. Late maturing (120 days), medium maturing (90-95 days), and early maturing variety (80-85 days). As a straightforward crop water deficit model. An adapted WRSI index was developed to characterize the effect of using different climatic and soil moisture remote sensing input datasets, such as CHIRPS rainfall, SMAP soil moisture at the top 5 cm and the root zone, MODIS actual evapotranspiration on key WRSI index parameters and outputs. Results from the analyses indicated that SMAP best captures season onset and length of the growing period, which are critical for the WRSI index. In addition, short-, medium-, and long-term sorghum cultivar planting scenarios were con-sidered and simulated. It was found that over half of the variability in yield is explained by water stress when the SMAP at root zone dataset is used in the WRSI model (R2=0.59–0.72 for sorghum varieties of 90–120 days growing length). Overall, CHIRPS and SMAP root zone show the highest skill (R2=0.53–0.64 and 0.54–0.56, respectively) in capturing state-level crop yield losses related to seasonal soil moisture deficit, which is critical for drought early warning and agrometeorological risk applications. The results of this study are important and valuable in supporting the continued development and improvement of satellite-based soil moisture sensing to produce higher accuracy soil moisture products in semi-arid regions. The results also highlight the growing awareness among various stakeholders of the impact of drought on crop production and the need to scale up adaptation measures to mitigate the adverse effects of drought.

From the Proceedings of the 1971 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - April 22-23, 1971, Tempe, Arizona / Knowledge of the dynamic water content-pressure potential relationship within the soil profile is useful in determining the importance of hysteresis under natural conditions. Continuous monitoring of water content in the field is now possible using recently developed gamma-ray transmission equipment which allows water content measurements in 1 cm-thick soil layers with an error of 0.0009 gm/gm. The nuclear equipment and the tensiometer assembly for pressure measurements are described. Soil water content and pressure in the top 10 cm of a field soil profile were measured continuously for a 2-week period following an irrigation. The highest water content was measured each day just before sunrise. This declined rapidly from early morning to early afternoon, and was followed by a gain during the mid-afternoon and evening. The amplitude of this diurnal change diminished with time after irrigation. The pressure potential at a depth of 1.5 cm decreased most rapidly as the water content declined, but not exactly in phase. This may have been due to temperature effects on the pressure metering system. A moisture characteristic curve was constructed from the data.

From the Proceedings of the 1975 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - April 11-12, 1975, Tempe, Arizona / Space-age technology has produced tools which when turned to earthly pursuits can provide information on food and fiber production. Soil moisture has the potential for being remotely assessed, and three techniques for accomplishing this are under study. Two of the methods, reflectance and thermal, are sensitive to the conditions of the bare soil surface. The third technique, microwave emission, appears to have a good potential for assessing soil moisture with depth, because of its greater wavelength.

Douglas-fir (Pseudotsuga menziesii) and lodgepole pine (Pinus contorta) in southwestern Alberta were studied to determine the water sources used and the effect of changing soil moisture on tree ecophysiological function. The hydrogen stable isotope ratios of water from local groundwater and precipitation were compared to tree stem water to determine the amount of stem water coming from those two sources. There were no significant differences between species in the portion of summer precipitation taken up. However, Douglas-fir shifted towards using more groundwater as shallow soil moisture declined. In addition, Douglas-fir showed large changes in shoot water potential, but maintained relatively constant rates of oxygen evolution, whereas lodgepole pine exhibited smaller changes in shoot water potential and had severely reduced rates of oxygen evolution during mid-summer drought. Lower leaf-area to sap-wood area and higher leaf δ13C (carbon isotope composition) suggested a less efficient hydraulic system in Douglas-fir compared to lodgepole pine. / x, 91 leaves : ill. ; 29 cm

There is a shared desire among public and private sectors to make more reliable predictions, accurate mapping, and appropriate scaling of soil moisture and associated parameters across landscapes. A discrepancy often exists between the scale at which soil hydrologic properties are measured and the scale at which they are modeled for management purposes. Moreover, little is known about the relative importance of hydrologic modeling parameters as soil moisture fluctuates with time. More research is needed to establish which observation scales in space and time are optimal for managing soil moisture variation over large spatial extents and how these scales are affected by fluctuations in soil moisture content with time. This research fuses high resolution geoelectric and light detection and ranging (LiDAR) as auxiliary measures to support sparse direct soil sampling over a 40 hectare inner BluegrassKentucky (USA) landscape. A Veris 3100 was used to measure shallow and deep apparent electrical conductivity (aEC) in tandem with soil moisture sampling on three separate dates with ascending soil moisture contents ranging from plant wilting point to near field capacity. Terrain attributes were produced from 2010 LiDAR ground returns collected at ≤1 m nominal pulse spacing. Exploratory statistics revealed several variables best associate with soil moisture, including terrain features (slope, profile curvature, and elevation), soil physical and chemical properties (calcium, cation exchange capacity, organic matter, clay and sand) and aEC for each date. Multivariate geostatistics, time stability analyses, and spatial regression were performed to characterize scale-dependent soil moisture patterns in space with time to determine which soil-terrain parameters influence soil moisture distribution. Results showed that soil moisture variation was time stable across the landscape and primarily associated with long-range (~250 m) soil physicochemical properties. When the soils approached field capacity, however, there was a shift in relative importance from long-range soil physicochemical properties to short-range (~70 m) terrain attributes, albeit this shift did not cause time instability. Results obtained suggest soil moisture’s interaction with soil-terrain parameters is time dependent and this dependence influences which observation scale is optimal to sample and manage soil moisture variation.

As ornamental container nurseries face diminishing water allocations, many are looking to automated irrigation solutions to increase their water application efficiency. This thesis presents the findings of a study conducted at a commercial container nursery to determine 1) whether a capacitance or load cell sensor was better suited for monitoring volumetric water content in the substrate; 2) if the actual irrigation distribution conformed to the expected pattern, how uniform were the weights of plants, and how these combined with plant canopy affected the leaching fraction; and 3) the reliability of the wireless network used to transmit the data to a central database. It was found that 1) the load cells outperformed the capacitance-based sensors because the load cells took an integrated measure; 2) the actual irrigation pattern followed the expected pattern, the variation of irrigation sections were low (C.V. = 0.06) and similar (C.V. ranging from 0.029 to 0.12), and unpruned plant canopies produced greater leaching fraction than pruned canopies (P < 0.18); and 3) wireless network transmission reliability was low (75.2%), suggesting that the system was not suitable for real-time irrigation control, but was sufficient for calculating irrigation length and monitoring net effective irrigation application and evapotranspirative consumption. / Graduation date: 2012

Environmental monitoring of Earth from space has provided invaluable information for understanding the land-atmosphere water and energy exchanges. However, the use of satellite observations in hydrologic applications is often limited by coarse space-time resolutions. This study aims to develop a data assimilation system that integrates remotely-sensed precipitation and soil moisture observations into physically-based models to produce fine-scale precipitation, soil moisture, and other relevant hydrometeorological variables. This is particularly useful with the active Global Precipitation Measurement and Soil Moisture Active Passive missions. The system consists of two major components: (1) a framework for dynamic downscaling of satellite precipitation products using the Weather Research and Forecasting (WRF) model with four-dimensional variational data assimilation (4D-Var) and (2) a variational data assimilation system using spatio-temporally varying background error covariance for directly assimilating satellite soil moisture data into the Noah land surface model coupled with the WRF model. The WRF 4D-Var system can effectively assimilate and downscale six-hour precipitation products of a spatial resolution of about 20 km (i.e., those derived from the National Centers for Environmental Prediction Stage IV data and the Tropical Rainfall Measuring Mission (TRMM) 3B42 dataset) to hourly precipitation with a spatial resolution of less than 10 km. The system is able to assimilate and downscale daily soil moisture products at a gridded 36-km resolution obtained from the Soil Moisture and Ocean Salinity (SMOS) mission to produce hourly 4-by-4 km surface soil moisture forecasts with a reduction of mean absolute error by 35% on average. The results from the system with coupled components show that assimilation of the TRMM 3B42 precipitation improves the quality of both downscaled precipitation and soil moisture analyses, while the effect of SMOS soil moisture data assimilation is largely on the soil moisture analyses. The downscaled WRF precipitation, with and without assimilation of TRMM precipitation, was preliminarily tested with a spatially distributed simulation of streamflow using the TIN (Triangular Irregular Network)-based Real-time Integrated Basin Simulator (tRIBS).