An algorithm to schedule water delivery in pressurized irrigation networks

This study presents a deterministic constrained optimisation algorithm developed for using in a pressurized irrigation network. In irrigation networks —or water networks supplied by a head tank— utility managers can fully adapt the delivery times to suit their needs. The program provides a strategy for scheduling water delivery at a constant flow rate (opening and closing of hydrants, units, and subunits) to minimise energy consumption. This technique improves on earlier approaches by employing a deterministic method with little computing time. This method has been tested in the University of Alicante pressurized irrigation network, where decision-makers have identified the need to diminish the energy expenditure for watering University’s gardens.This work was supported by the research project “DESENREDA” through the 2021 call “Estancias de movilidad en el extranjero Jose Castillejo” of the Ministerio de Universidades (CAS21/00085) and for the project “Hi-Edu Carbon” Erasmus Plus Programme, Key Action KA22021, action type (2021-1-SK01-KA220-HED-000023274

Field Deployment and Integration of Wireless Communication & Operation Support System for the Landscape Irrigation Runoff Mitigation System

The study of water conservation technologies is critically important due to the rapid growth in urban population leading to a shortage in potable water supplies throughout the world. Current water supplies are not expected to meet the water demand in the coming decades; this could seriously affect human lives and socio-economic stability. About 30 percent of the current municipal supplies are being used for outdoor irrigation such as gardening and landscaping. These numbers are increasing due to the increase in urban population. Due to the current inefficient or improper landscape irrigation practices, substantial amounts of water are lost in the form of runoff or due to evaporation. Runoff occurs when the irrigation precipitation rate exceeds the infiltration rate of the soil, which depends on the soil and site characteristics such as soil type and the slope of the site. Runoff being an obvious water wastage, it also poses a great problem to the environment with its potential for transporting fertilizers and pesticides into storm sewers and, eventually, surface waters. Thus, this study focuses on designing a smart operational support system for landscape irrigation that has the potential to reduce runoff and also decrease water losses in the form of evaporation. The system consists of two main units, the landscape irrigation runoff mitigation system (LIRMS) and an operational support system (OSS). The combined system is referred to as the second-generation LIRMS. The LIRMS is installed at the border of a field/lawn. The LIRMS consists of a central controller unit and a runoff sensor. Based on the feedback from the runoff sensor, the controller unit pauses and resumes irrigation as needed in order to reduce runoff. The main purpose of OSS is to automate the scheduling of the irrigation process. A multilayer perceptron based OSS was designed and implemented on a dedicated web-server. The OSS processes historical irrigation data and the environmental/weather data to choose an optimal schedule to irrigate on a given day. The OSS aims to reduce irrigation water losses due to natural environmental factors such as evaporation and rain. A wireless communication link is established between LIRMS and OSS for monitoring and analyzing irrigation events. The second-generation LIRMS was installed in the Texas A&M Turfgrass Research Field Laboratory, College Station, TX for performing irrigation tests. The preliminary results show that the average soil wetting efficiency has increased with the use of the operational support system when compared to previous tests performed without the operational support system. Also the results suggest that the second generation LIRMS has comparable runoff reductions when compared to the first-generation LIRMS. Yet, more tests are required to quantify the overall water savings

Evaluating Irrigation Efficiency with Performance Indicators: A Case Study of Citrus in the East of Spain

[EN] Improving water efficiency in farming systems is one of the major challenges of these decades. Water scarcity due to climate change, together with the increasing demand of food, is leading experts from around the world find appropriate indicators for water-use efficiency. In this paper we propose and test different indicators for service delivery performance, productive efficiency, and economic efficiency. Since the characteristics of the studied area and the citrus cropping system in the East of Spain are particular, we include in our analysis two other variables which are key to understanding the changes in the indicators: the obtained productivity, and the applied irrigation. The indicators and these two variables are tested with the information provided by farmers of citrus orchards belonging to an irrigation community from the East of Spain. The effect of different factors, such as cultivated varieties, type of farmer (professional or non-professional), or plantations' size, are evaluated against the productivity and irrigation performance of the evaluated orchards. The effect of excess of irrigation on the indicators is also studied with the previous factors. Finally, an artificial intelligence system is used to predict productive efficiency of an orchard, based on the size and the water supply. Among the proposed indicators, the service delivery performance indicators came out to be the least useful and might provoke overirrigation due to the lack of accuracy of the data used for its calculation. The productive and economic efficiency indicators have been useful to illustrate the remarkable effect that excess of irrigation has on water efficiency, since a reduction of 66% of productive efficiency is found for some of the analysed varieties. On other cases, a reduction of 50% in economic efficiency is detected due to the excess of irrigation. Moreover, the excess of irrigation implied higher economic efficiency in only one of the evaluated varieties.This work was partially funded by the European Union through ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR and by Conselleria de Educacion, Cultura y Deporte through "Subvenciones para la contratacion de personal investigador en fase postdoctoral", grant number APOSTD/2019/04.Parra-Boronat, L.; Botella-Campos, M.; Puerto, H.; Roig-Merino, B.; Lloret, J. (2020). Evaluating Irrigation Efficiency with Performance Indicators: A Case Study of Citrus in the East of Spain. Agronomy. 10(9):1-28. https://doi.org/10.3390/agronomy10091359S12810

Data-driven models for canopy temperature-based irrigation scheduling

Normalized crop canopy temperature, termed crop water stress index (CWSI), was proposed over 40 years ago as an irrigation management tool but has experienced limited adopted in production agriculture. Development of generalized crop-specific upper and lower reference temperature is critical for implementation of CWSI-based irrigation scheduling. The objective of this study was to develop and evaluate data driven models for predicting reference canopy temperatures needed to compute CWSI for sugarbeet and wine grape. Reference canopy temperatures for sugarbeet and wine grape were predicted using machine learning and regression models developed using measured canopy temperatures of sugarbeet, grown in Idaho and Wyoming, and wine grape, grown in Idaho and Oregon, over 5 years under full and severe deficit irrigation. Lower reference temperatures were estimated using neural network models with Nash-Sutcliffe model efficiencies exceeding 0.88 and root mean square error less than 1.1 degree Celsius. The relationship between well-watered canopy temperature minus ambient temperature and vapor pressure deficit was represented by a linear model that maximized the regression coefficient rather than minimized the sum of squared error. The linear models were used to estimate upper reference temperatures nearly double values reported in previous studies. Daily CWSI calculated as the average of 15-min values determined between 13:00 and 16:00 MDT for sugarbeet and 13:00 and 15:00 local time for wine grape was well correlated with irrigation events and amounts. A quadratic relationship between daily CWSI and midday leaf water potential of Malbec and Syrah wine grape was significant (p<0.001) with an R2 of 0.67. The data driven models developed in this study to estimate reference temperatures permit automated calculation of CWSI for effective assessment of crop water stress, however, wet canopy conditions or solar radiation < 200 W m-2 can result in irrational values of CWSI. Automated calculation of CWSI using the methodology of this study would need to check for wet canopy or low solar radiation conditions and omit calculation of CWSI if determined to be probable

Reviewing the Options for the Agricultural Sector to Adapt to Climate Change: Case Study of the Niagara Region, ON

The agricultural sector of the Niagara Region has experienced multiple impacts of climate change in recent years, which are projected to increase in the future. There is an urgent need to examine available adaptation strategies for Niagara’s agricultural sector, considering its vulnerability to a changing climate and significance for the Region’s economy and food production. Using a scoping review of scientific literature to analyze 4375 articles on two databases, this research has investigated four potential adaptation strategies - i.e. technology-based adaptation, ecosystem-based adaptation, community-based adaptation and policy-based adaptation - that can be used by the agricultural sector. All adaptation strategies were also examined through a social, economic and environmental lens using a SWOT Analysis. Through this statement, this research also highlights its contribution to sustainability science and sustainable development (SDG 2 – Food Security and SDG 13 – Climate Action) as one of the steps towards a more resilient future

Advanced monitoring and management systems for improving sustainability in precision irrigation

Globally, the irrigation of crops is the largest consumptive user of fresh water. Water scarcity is increasing worldwide, resulting in tighter regulation of its use for agriculture. This necessitates the development of irrigation practices that are more efficient in the use of water but do not compromise crop quality and yield. Precision irrigation already achieves this goal, in part. The goal of precision irrigation is to accurately supply the crop water need in a timely manner and as spatially uniformly as possible. However, to maximize the benefits of precision irrigation, additional technologies need to be enabled and incorporated into agriculture. This paper discusses how incorporating adaptive decision support systems into precision irrigation management will enable significant advances in increasing the efficiency of current irrigation approaches. From the literature review, it is found that precision irrigation can be applied in achieving the environmental goals related to sustainability. The demonstrated economic benefits of precision irrigation in field-scale crop production is however minimal. It is argued that a proper combination of soil, plant and weather sensors providing real-time data to an adaptive decision support system provides an innovative platform for improving sustainability in irrigated agriculture. The review also shows that adaptive decision support systems based on model predictive control are able to adequately account for the time-varying nature of the soil–plant–atmosphere system while considering operational limitations and agronomic objectives in arriving at optimal irrigation decisions. It is concluded that significant improvements in crop yield and water savings can be achieved by incorporating model predictive control into precision irrigation decision support tools. Further improvements in water savings can also be realized by including deficit irrigation as part of the overall irrigation management strategy. Nevertheless, future research is needed for identifying crop response to regulated water deficits, developing improved soil moisture and plant sensors, and developing self-learning crop simulation frameworks that can be applied to evaluate adaptive decision support strategies related to irrigation

DEVELOPMENT OF A MACHINE LEARNING ALGORITHM TO MINIMIZE RUNOFF THROUGH AN AUTOMATED SMART IRRIGATION SYSTEM

The study of proper water management practices is of prime importance due to the ever- increasing population and rapid industrialization which results in shortage of portable water supplies throughout the world. The current water supplies are not expected to meet the increasing demand in the upcoming decades which could in result affect the socio-economic stability and have a detrimental effect on human livelihood. About 30% of the current municipal supplies in the world are used for outdoor irrigation activities such as gardening and landscaping purposes. These numbers are on the rise due to the ever increasing human population. Due to the current inefficient landscape practices, substantial amount of water is lost in the form of runoff. This poses a great threat to the environment with its potential for transporting fertilizers and pesticides into storm sewers and, eventually, surface waters. Thus, this study focuses on designing a Machine Learning approach which would act as a Decision Support System (DSS) to irrigate turfgrasses to minimize runoff in the plots while maintaining the quality of the turfgrasses. For this, a robust Machine Learning approach named as Radial Basis Function - Support Vector Machine (RBF-SVM) was proposed which was trained on the synthetic data generated from the datapoints recorded during the year 2015-16 and 2016-17 at the Turfgrass Laboratory in Texas A & M University, College Station. For each of the approaches, the target variable was changed and the number of features were varied in each case to see which gives the best results. Among all the target variables, predicting the Soil Wetting Efficiency Index, devised by Wherley, et. al.[33] was the most applicable as it is one of the most generic approaches since it is not site-specific and gave the highest validation testing accuracy of 90%. Thus, the latter approach was used in the ASIS controller to observe the robustness of the algorithm in controlling the effectiveness of the irrigation cycle. Until now, only few irrigation cycles have been scheduled and experimental data are still being collected from the facility. Preliminary results suggested that the Machine Learning algorithm has the potential to save water as it helped in efficient regulation of irrigation cycles and even achieved a goal of zero runoff in two of the irrigation runs. The Green Cover percentage of the plots where the proposed ASIS controller was mounted showed an increment of about 12%, thereby validating the fact that the quality of turfgrasses was also maintained. With more irrigation cycles which would be scheduled over time, the proposed Machine Learning approach is expected to perform better with increase in observations and may nullify runoff eventually