Internet of Things (IoT) for Soil Moisture Tensiometer Automation.

Monitoring of water retention behavior in soils is an essential process to schedule irrigation. To this end, soil moisture tensiometers usually equipped with mechanical manometers provide an easy and cost-effective monitoring of tension in unsaturated soils. Yet, periodic manual monitoring of many devices is a tedious task hindering the full exploitation of soil moisture tensiometers. This research develops and lab validates a low cost IoT soil moisture tensiometer. The IoT-prototype is capable of measuring tension up to -80 Kpa with R2 = 0.99 as compared to the same tensiometer equipped with a mechanical manometer. It uses an ESP32 MCU, BMP180 barometric sensor and an SD card module to upload the measured points to a cloud service platform and establishes an online soil water potential curve. Moreover, it stores the reading on a micro-SD card as txt file. Being relatively cheap (76 USD) the prototype allows for more extensive measurements and, thus, for several potential applications such as soil water matric potential mapping, precision irrigation, and smart irrigation scheduling. In terms of energy, the prototype is totally autonomous, using a 2400 mAh Li-ion battery and a solar panel for charging, knowing that it uses deep sleep feature and sends three data points to the cloud each 6 h.

Bacterial Inoculants Mitigating Water Scarcity in Tomato: The Importance of Long-Term in vivo Experiments.

Global population growth and climate change raise a challenge to agriculture, which, combined with the issues concerning the use of chemical fertilizers, have generated increasing attention in the use of plant-associated bacteria as a sustainable strategy in agri-food systems. The objective of this study is to evaluate the ability of five bacterial strains, previously isolated from the rhizosphere or endosphere of plants adapted to harsh environmental conditions, to act as potential plant biofertilizers in different conditions of water availability. The strain biosafety for a deliberate environmental release was investigated through a literature survey and antibiotic resistance testing. The selected strains were first characterized for their plant growth-promoting (PGP) and rhizocompetence-related traits through in vitro assays and then on short-term in vivo experiments on tomato plants. A long-term greenhouse experiment was further conducted to monitor the PGP effect of the bacteria during the entire life cycle of tomato plants subjected to full irrigation or to severe water deficit conditions, aiming to assess their actual effect on plant productivity, which is the ultimate target of the agricultural sector. Some of the strains showed a potential in improving water use efficiency and mitigating plant water stress. Under severe irrigation deficit, four of the tested strains, Micrococcus yunnanensis M1, Bacillus simplex RP-26, Pseudomonas stutzeri SR7-77, and Paenarthrobacter nitroguajacolicus 2-50, significantly increased the number of productive plants in comparison to non-bacterized control ones. Two of them, Bacillus simplex RP-26 and Paenarthrobacter nitroguajacolicus 2-50, demonstrated also, under full irrigation, to significantly improve the water productivity in comparison with non-bacterized plants. Despite all the strains showed promising PGP potential in short-term assays, the positive effect of the bacterial inoculants on plant physiology and fruit yield was observed in some cases but never corroborated by statistical significance. These results highlight the importance of performing long-term in vivo experiments to define the real PGP ability of a bacterial inoculant to positively impact plant production.

Development and Application of a Predictive Model for Treated Wastewater Irrigation Management in a Semiarid Area.

The use of treated wastewater (TWW) as an alternative resource to fresh water (FW) for irrigation purposes is becoming increasingly important, especially in semiarid and arid regions. However, achieving success in crop production largely depends on the adoption of appropriate on-farm management strategies aimed at optimizing crop yields, maintaining soil productivity and safeguarding the environment. For this purpose, predictive models are of particular interest. A safe irrigation management (SIM) model developed and tested in this research was used to schedule irrigation under controlled management tailored to the use of 1) TWW and 2) FW and to reproduce farmers' strategies. These management strategies aim to improve actual irrigation practices, accounting for water quality, soil characteristics, and crop yield. The results of the application of SIM on a citrus farm in Souss-Massa, Morocco, show that the management strategy adopted by farmers withdraws considerable amounts of water and results in substantial drainage volumes compared to those in the SIM strategy. In the specific case of TWW, the strategy simulated by the SIM model resulted in a decrease in yield of approximately 4%, compared to the 23% decrease derived from the farmers' traditional strategy. Moreover, SIM allowed for great savings in terms of fertilizing elements and for the reduction in the movement of water and salts beyond the root zone, usually considered the main source of groundwater contamination. These results confirm the appropriateness of using prediction models and the accuracy of the SIM model in adapting irrigation strategies to TWW, which will be an integral part of the strategies that encourage their use in irrigated agriculture. Integr Environ Assess Manag 2020;16:910-919. © 2020 SETAC.