Control of Substrate Water Availability Using Soil Sensors and Effects of Water Deficit on the Morphology and Physiology of Potted Hebe andersonii

Many plant producers tend to overwater crops to prevent water stress and salt-induced damage. These practices waste irrigation water and cause leaching that harms the environment and increases production costs. In order to optimize water consumption and minimize the environmental impact of plant production, this study aimed to determine the physiological and morphological responses of Hebe andersonii to three substrate volumetric water contents (49%, 39%, and 32%). The experiment was conducted in a greenhouse with an irrigation protocol that consisted of adding small volumes of water to avoid leaching while monitoring substrate moisture with dielectric soil sensors. The results showed that moderately low substrate moisture improved the water-use efficiency, while growth was significantly reduced under more severe water deficit conditions (but without leaf chlorosis or abscission). The photosynthetic activity of Hebe was primarily controlled by the stomatal aperture, which was co-determined by the substrate moisture and seasonal temperature. Hebe leaves promoted non-photochemical quenching when carbon assimilation was limited by a water deficit, and accumulated solutes through an osmotic adjustment process (especially Cl−, Na+, and K+) to maintain their water status. Overall, Hebe andersoni cv. Variegata could successfully grow and improve its water-use efficiency in low substrate moisture and under a non-draining irrigation regime.

Validation of Rapid and Low-Cost Approach for the Delineation of Zone Management Based on Machine Learning Algorithms

Proximal soil sensors are receiving strong attention from several disciplinary fields, and this has led to a rise in their availability in the market in the last two decades. The aim of this work was to validate agronomically a zone management delineation procedure from electromagnetic induction (EMI) maps applied to two different rainfed durum wheat fields. The k-means algorithm was applied based on the gap statistic index for the identification of the optimal number of management zones and their positions. Traditional statistical analysis was performed to detect significant differences in soil characteristics and crop response of each management zones. The procedure showed the presence of two management zones at both two sites under analysis, and it was agronomically validated by the significant difference in soil texture (+24.17%), bulk density (+6.46%), organic matter (+39.29%), organic carbon (+39.4%), total carbonates (+25.34%), total nitrogen (+30.14%), protein (+1.50%) and yield data (+1.07 t ha−1). Moreover, six unmanned aerial vehicle (UAV) flight missions were performed to investigate the relationship between five vegetation indexes and the EMI maps. The results suggest performing the multispectral images acquisition during the flowering phenological stages to attribute the crop spatial variability to different soil proprieties.

Design and Build a Soil Nutrient Measurement Tool for Citrus Plants Using NPK Soil Sensors Based on the Internet of Things

A suitable planting medium is a medium of good quality soil that can support plant growth quickly. Fertile soil is the primary need for plants. The quality of the planting medium dramatically affects plant growth. The types of soil needed for plant growth vary, namely sandy soil, red soil, alluvial soil, and humus soil. Using an NPK sensor that functions to detect nutrients in the soil and can work if the tip of the sensor is plugged into the soil they want to detect, the results detected by the sensor will be sent in the form of analog signal data to nodemcu, which will be processed and displayed on the screen. Thingspeak. This tool is controlled by nodemcu with an NPK sensor to detect nutrients in the soil with output to thingspeak. The function of the NPK sensor tool will measure the nutrients in the soil for citrus seedlings, and the results read by the Npk sensor will be sent to the Thingspeak web, making it easier for farmers to seed citrus seeds. The data read by the sensor will be sent to thingspeak, making it easier to monitor nutrients in the soil. From the results of the tests carried out, it is found that the nutrient content in wet soil is higher than in dry soil; from the tests carried out, the NPK sensor accuracy rate is 90%.

Peering into the internal structure of cold pools and their interactions with a dense observational network

<p>Melting and evaporation of hydrometeors in and below convective clouds generates cold, dense air that falls through the atmospheric column and spreads at the surface like a density current, the cold pool. In modelling studies, the importance of cold pools in controlling the lifecycle of convection has often been emphasized, being through their organization of the cloud field or through their sheer deepening of the convection. Larger, longer-lived cold pools benefit convection, but little is actually known on the size and internal structure of cold pools from observations as the majority of cold pools are too small to be captured by the operational surface network.  One aim of the field campaign FESSTVaL was to peer into the internal structure of cold pools and their interactions with the underlying land surface by deploying a dense network of surface observations. This network consisted of 80 self-designed cold pool loggers, 19 weather stations and 83 soil sensors deployed in an area of 15 km around Lindenberg. FESSTVaL took place from 17 May to 27 August 2021.</p> <p>In principle, cold pool characteristics are affected both by the atmospheric state, which fuels cold pools through melting and evaporation of hydrometeors, and the land surface, which acts to destroy cold pools through friction and warming by surface fluxes. In this talk, the measurements collected during FESSTVaL will be used to shed light on these interactions.  We are particularly interested to assess how homogeneous the internal structure of cold pools is and whether heterogeneities of the land surface imprint themselves on this internal structure. The results will be compared to available model simulations.</p>