Field assessment of interreplicate variability from eight electromagnetic soil moisture sensors

Interreplicate variability—the spread in output values among units of the same sensor subjected to essentially the same condition—can be a major source of uncertainty in sensor data. To investigate the interreplicate variability among eight electromagnetic soil moisture sensors through a field study, eight units of TDR315, CS616, CS655, HydraProbe2, EC5, 5TE, and Teros12 were installed at a depth of 0.30 m within 3 m of each other, whereas three units of AquaSpy Vector Probe were installed within 3 m of each other. The magnitude of interreplicate variability in volumetric water content (θv) was generally similar between a static period near field capacity and a dynamic period of 85 consecutive days in the growing season. However, a wider range of variability was observed during the dynamic period primarily because interreplicate variability in θv increased sharply whenever infiltrated rainfall reached the sensor depth. Interreplicate variability for most sensors was thus smaller if comparing θv changes over several days that excluded this phenomenon than if comparing θv directly. Among the sensors that also reported temperature and/or apparent electrical conductivity, the sensors exhibiting the largest interreplicate variability in these outputs were characterized by units with consistently above or below average readings. Although manufacturers may continue to improve the technology in and the quality control of soil moisture sensors, users would still benefit from paying greater attention to interreplicate variability and adopting strategies to mitigate the consequences of interreplicate variability

Potential of Densification of Mango Waste and Effect of Binders on Produced Briquettes

In Uganda, agro-processing of fruits produces large volumes of agricultural wastes much of which are not utilized but disposed in the landfill. This study explored the possibility of producing biomass briquettes from mango waste (seed covers) that could be used for energy supply in small factories and for domestic cooking. Dried mango seed covers were crushed to particles of size 2 mm and bonded with three different binders; starch, starch-clay soil, and starch-red soil. The best mixing ratios for briquettes were; 4:1 (seed-cover: starch), 9:2:1 (seed cover: starch: clay soil), and 16:4:1 (seed-cover: starch: red soil). The formed briquettes were subjected to several standard methods to verify their suitability as fuels. The briquette properties tested were moisture content, volatile matter, ash content, fixed carbon content, calorific value, compressive strength, and gaseous emissions. Results showed that briquettes bonded with only starch had a significantly (p ≤ 0.05) higher fuel properties with low: moisture content (11.9%), volatile matter (16.0%), ash content (2.8%) and emissions (0.178% CO, 0.0021% (CH)X , 1.14% CO2 and no NOx); higher fixed carbon (69.3%), breaking strength (maximum force, 34 N and compressive stress, 273 N/mm2) and calorific values (16,140 KJ/Kg)  compared to starch-red soil and starch-clay soil briquettes.  But after a linear regression analysis, results further showed that maximum force (R2 = 0.636) and ash content (R2 = 0.520) were good indicators of energy content of a particular briquette. However, more research is needed on using other binder types rather than cassava starch which is considered as food

A critical analysis of physiochemical properties influencing pit latrine emptying and feacal sludge disposal in Kampala Slums, Uganda

Inadequate information on physiochemical properties of faecal sludge leads to inappropriate design of pit emptying devices and poor faecal sludge disposal contributing to environmental pollution. This study undertook a critical analysis of physiochemical properties of feacal sludge that influence design and performance of pit emptying devices and faecal sludge disposal for improved faecal sludge management in urban slums. The physiochemical properties determined were; Moisture content (MC), ash content (AC), total solids (TS), volatile solids (VS), nitrogen (N), phosphorous (P), potassium (K) and pH. Samples were collected from 55 unlined pits at depths of 0, 0.5, 1 and 1.5 m from pit surface. The unlined pits in this study were purposively selected from slums in Kampala. A sample of 300 g was sucked from each depth using a manual sampling tool and emptied into a plastic container. The container was then wrapped in a black plastic bag and transported in cooler boxes to the lab for analysis. The properties were subjected to Principal Component Analysis to isolate the critical parameters that affect pit emptying and faecal sludge disposal. The mean results were: MC of 86 ± 8.37%; TS of 0.14 ± 0.08 g/g wet sample; VS of 0.73 ± 0.32 g/g dry sample; pH of 8.0 ± 1.5; AC of 0.35± 0.18 g/g dry sample; TN of 3.5 ± 0.08%; K of 2.2± 0.13% and P of 1.4± 0.05%. It was concluded that physiochemical properties in Ugandan pits are comparable to those of global pits except for the acidic conditions at top surface in some pits, and higher moisture content in pits due to the high water table. PCA results showed that moisture content and total solids affected pit emptying techniques while fractional content of N, P and pH affect most choice of faecal sludge disposal technique.Key words: Pit latrine, faecal sludge management, developing countries, physiochemical properties, pit emptying

Rainwater harvesting knowledge and practice for agricultural production in a changing climate: A review from Uganda’s perspective

With a changing climate in Uganda, rainfall distribution patterns have become more irregular over time and space. Excess water during rainy season is causing runoff, soil erosion, nutrient depletion and crop damage which reduce the productive capacity of land, while on the other hand, prolonged droughts during the crop growing period have become common occurrences. Additionally, pastoralists lose livestock during the dry period each year in the Cattle Corridor of Uganda due to water shortage and lack of forage. It thus remains difficult to achieve the agricultural development targets identified in the National Development Plan for Uganda, without addressing regular incidences of adverse impacts of climate change. Currently there are no well explained approaches which can contribute to adoption of technologies like rainwater management systems which are crucial in enhancing crop yields and livestock production during periods of water shortage. The overarching objective of this paper was to carry out an assessment of the status, performance, and scope for improving rainwater harvesting (RWH) for small-scale agriculture under local conditions. Accordingly, research gaps in RWH technologies were identified and documented to inform future studies. The research was carried out in the semi-arid areas of Nakasongola, Rakai, and Hoima Districts characterized by crop-livestock dependent livelihoods. Findings show that RWH Technologies can enable smallholder farmers and agro-pastoralists to become more resilient to increasing climate variability and climate change by conserving soil and water thus increasing food production and enhancing food security. Small-scale irrigation systems have enabled farmers adapt to drought challenges by enhancing crop yields and allowing farmers to target for higher market prices usually associated with the effects of drought. However, there are challenges including threats to sustainability of such established systems because of lack of community participation in systems’ monitoring and maintenances, and vandalism, and some systems require high investment costs.

Field assessment of interreplicate variability from eight electromagnetic soil moisture sensors

Interreplicate variability—the spread in output values among units of the same sensor subjected to essentially the same condition—can be a major source of uncertainty in sensor data. To investigate the interreplicate variability among eight electromagnetic soil moisture sensors through a field study, eight units of TDR315, CS616, CS655, HydraProbe2, EC5, 5TE, and Teros12 were installed at a depth of 0.30 m within 3 m of each other, whereas three units of AquaSpy Vector Probe were installed within 3 m of each other. The magnitude of interreplicate variability in volumetric water content (θv) was generally similar between a static period near field capacity and a dynamic period of 85 consecutive days in the growing season. However, a wider range of variability was observed during the dynamic period primarily because interreplicate variability in θv increased sharply whenever infiltrated rainfall reached the sensor depth. Interreplicate variability for most sensors was thus smaller if comparing θv changes over several days that excluded this phenomenon than if comparing θv directly. Among the sensors that also reported temperature and/or apparent electrical conductivity, the sensors exhibiting the largest interreplicate variability in these outputs were characterized by units with consistently above or below average readings. Although manufacturers may continue to improve the technology in and the quality control of soil moisture sensors, users would still benefit from paying greater attention to interreplicate variability and adopting strategies to mitigate the consequences of interreplicate variability

Real-time irrigation scheduling of maize using Degrees Above Non-Stressed (DANS) index in semi-arid environment

Irrigation scheduling methods have been used to determine the timing and amount of water applied to crops. Scheduling techniques can include measurement of soil water content, quantification of crop water use, and monitoring of crop physiological response to water stress. The aim of this study was to evaluate the performance of a simplified crop canopy temperature measurement (CTM) method as Irrigation Principles. Soil and Water Conservation Engineera technique to schedule irrigation for maize. Specifically, the Degrees Above Non-Stressed (DANS) index, which suggests water stress when canopy temperature exceeds the non-stressed canopy temperature (Tcns), was determined by estimating Tcns from a weather based multilinear regression model. The modeled Tcns had a strong correlation with observed Tcns with a pooled R2 values of 0.94 across the 2018, 2019, and 2020 growing seasons. This DANS index was also highly correlated with the conventionally used Crop Water Stress Index (CWSI) with R2 values of 0.67, 0.59, and 0.76 in 2018, 2019, and 2020, respectively. Furthermore, DANS had a strong linear relationship with soil water depletion above 60% in the 0.60 m soil profile with an R2 of 0.78. The CTM method was also compared to more commonly used scheduling methods namely: soil moisture monitoring (SMM) and crop evapotranspiration modeling (ETM). Grain yield was significantly lower for the CTM method than for the ETM method in 2018 and 2020 but not in 2019. No significant differences were observed in Irrigation Water Productivity (IWP) in 2018; however, all treatments were significantly different with the CTM method having the greatest IWP in 2020. For attempting to trigger full irrigation with the CTM method, a fixed DANS threshold of 0.5 ◦C was found to be more appropriate than the literature value of 1.0 ◦C, but consideration of crop growth stage would further improve scheduling

Development of a Scalable Edge-Cloud Computing Based Variable Rate Irrigation Scheduling Framework

Currently, variable-rate precision irrigation (VRI) scheduling methods require large amounts of data and processing time to accurately determine crop water demands and spatially process those demands into an irrigation prescription. Unfortunately, irrigated crops continue to develop additional water stress when the previously collected data is being processed. Machine learning is a helpful tool, but handling and transmitting large datasets can be problematic; more rural areas may not have access to necessary wireless data transmission infrastructure to support cloud interaction. The introduction of “edge-cloud” processing to agricultural applications has shown to be effective at increasing data processing speed and reducing the amount of data transmission to remote processing computers or base stations. In irrigation in particular, edge-cloud computing has so far had limited implementation. Therefore, an initial logic flow concept has been developed to effectively implement this new processing technique for VRI. Utilizing edge-cloud computer nodes in the field, autonomous data collection devices such as center pivot-mounted infrared canopy thermometers, soil moisture sensors, local weather stations, and UAVs could transmit highly localized crop data to the edge-cloud computer for processing. The edge computer Following the implementation of an irrigation strategy created by the edge-cloud computer with a machine learning model, data would be transmitted to the cloud (requiring transmission of only minimal model parameters), resulting in a feedback loop for continual improvement of the global model on the cloud (federated learning). VRI prescription maps from the SETMI model were used as the training data for training the machine learning model

Differences in soil water changes and canopy temperature under varying water × nitrogen sufficiency for maize

Crop nitrogen (N) status is known to affect crop water status and crop water use. To investigate further the N effects on soil water changes and on canopy temperature, three water levels × four N levels were imposed on two growing seasons of maize in west central Nebraska, USA. Soil water changes were measured using a neutron probe, whereas canopy temperature was measured using infrared thermometers on a ground-based mobile platform. At all water levels, soil water losses over monthlong intervals were generally greater as N levels increased. Given equal water levels, early afternoon canopy temperatures were usually lower with higher N levels, but no trend or even the opposite trend was occasionally observed. Jointly considering canopy reflectance and soil water depletion shows potential to explain much of the variation in estimated instantaneous water use among plots. However, determining the relative contributions of the canopy and soil factors on a particular day may require season-to-date knowledge of the crop. Further research on assimilating such sensor data for a combined stress coefficient would improve crop modeling and irrigation scheduling when variable water sufficiency and variable N sufficiency are simultaneously significant

An Edge-computing flow meter reading recognition algorithm optimized for agricultural IoT network

Groundwater resources in Nebraska, U.S. are closely monitored by 23 Natural Resources Districts (NRDs) located across the state. Growers who use groundwater for irrigation are required to have flow meters installed at wells to monitor their water usage. However, many of these flow meters are still being read and recorded through in-person visits, which can be time-consuming and costly. Although some flow meters in Nebraska are monitored remotely by telemetry-enabled camera systems, yearly telemetry costs are high and making long-term operation financially burdensome. Using less expensive network protocol, such as Internet of Things (IoT), to transmit flow meter readings could enable new monitoring opportunities. However, there are challenges in directly transmitting flow meter images via IoT due to limited bandwidth. Therefore, in this study, we developed an algorithm using object detection deep learning techniques, i.e. You Only Look Once (YOLO) that can be programmed at an IoT node which can recognize readings from images of flow meters onsite before transmitting. The developed algorithm could significantly reduce data size and is essential for flow meter monitoring in an IoT network setting. The developed algorithm achieved 95.35% accuracy when recognizing 1,248 real-world flow meter images obtained at the courtesy of North Platte Natural Resources District (NPNRD) in western Nebraska. The framework and algorithm were also tested in a real-world scenario on a flow meter installed on a linear-move sprinkler irrigation system and showed promising results. By leveraging IoT and deep learning techniques, this research has the potential to revolutionize flow meter monitoring, reducing costs and improving efficiency in the management of groundwater resources in Nebraska, and potentially in other regions as well

Differences in soil water changes and canopy temperature under varying water × nitrogen sufficiency for maize

Crop nitrogen (N) status is known to affect crop water status and crop water use. To investigate further the N effects on soil water changes and on canopy temperature, three water levels × four N levels were imposed on two growing seasons of maize in west central Nebraska, USA. Soil water changes were measured using a neutron probe, whereas canopy temperature was measured using infrared thermometers on a ground-based mobile platform. At all water levels, soil water losses over monthlong intervals were generally greater as N levels increased. Given equal water levels, early afternoon canopy temperatures were usually lower with higher N levels, but no trend or even the opposite trend was occasionally observed. Jointly considering canopy reflectance and soil water depletion shows potential to explain much of the variation in estimated instantaneous water use among plots. However, determining the relative contributions of the canopy and soil factors on a particular day may require season-to-date knowledge of the crop. Further research on assimilating such sensor data for a combined stress coefficient would improve crop modeling and irrigation scheduling when variable water sufficiency and variable N sufficiency are simultaneously significant