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From the latest FAO report (SOFA, 2020), it is estimated that more than 70 percent of water use is accounted for by agriculture. These estimates will tend to grow, considering the increase in world population and demand for food.
Over the past two decades, freshwater supplies worldwide have declined by more than 20 percent; a trend that is unlikely to change given climate change and its effects. Poorly distributed rainfall, droughts, and high temperatures are indeed putting a strain on cultivation.
The areas most subject to the effects of climate change are those in southern Europe (Italy, Spain, Portugal, southern France, Greece) where semi-arid or arid conditions require the use of irrigation to obtain qualitatively and quantitatively acceptable agricultural production.
In recent years, due to regulations related to increasing input use efficiency, there has been increased interest in Decision Support Systems (DSS - Decision Support Systems). xFarm Technologies ' predictive models are conceived, designed, calibrated and validated to provide farmers and supply chains with an additional technical tool to support them in their activities.
Among the activities of xFarm Technologies, there is the support of clients in multi-year paths for the creation-customization of:
The experimentation took place in collaboration with Agriveneto Spa, a leading company in potato production and marketing located in the Padovano area. Three irrigation strategies were applied and compared and evaluated in terms of quantity and quality of final production. All with the aim of customizing and calibrating the irrigation DSS on base crop, environmental, soil and farm needs. The crop of interest is the table potato (Solanum tuberosum L.), variety Melrose. The tubers were sown in February 2022 on a loamy-clayey soil with a planting size of 0.22 m x 0.9 m, and harvesting began in July. Irrigation was accomplished by mini-sprinkler.
Three different irrigation strategies were applied on three experimental plots:
The percentage of ETc (crop evapotranspiration) return was chosen so as to impose two different levels of stress along the season; in addition, in theses 1 and 2 the Irrigation DSS of xFarm Technologies returned the "Irrigation Advice" and information about when and how much to irrigate. These indications represent the outputs of the models that took into account the farm's climatic variables, soil, crop and varietal characteristics.
Sensors installed in the field include the Weather Station xSense Pro (essential for collecting contextualized weather variables such as temperature, rainfall, relative humidity, solar radiation, wind direction and speed); a Teros 10 Dual Depth Sensor (20 cm - 40 cm) per plot (point measurement ofsoil moisture); and a Teros 21 Dual Depth Sensor (20 cm - 40 cm) per plot (point measurement of water potential).
During the season, in addition to the constant collection of data (with 30 min frequency) measured by the installed sensors, the plots were monitored on a cadenced basis and sampling was carried out for phenological mapping of the variety with respect to GDDs (Growing Degree Days) and for evaluation of plant appearance and behavior (at es. in terms of vigor, presence of abiotic and/or biotic symptoms) with different irrigation inputs.
Surveys were conducted close to harvest to assess production and the percentage of waste/parcel size potatoes (potatoes with non-commercially accepted size).
Chart 1 shows the 2022climatic trends recorded by the weather station located near the experimental plots. The precipitation that occurred and the estimated crop evapotranspiration (ETc) accumulated at each phenological stage are also shown.
Chart 1. Climatic trend 2022 of Stork (PD) with breakdown of the vintage by phenological stage. Sums of total precipitation (mm) and sum of Etc (mm) are shown for each phenological phase.
In 2022, cumulative rainfall during the period useful for cultivation (April to July) was 97 mm compared with 400 mm for the historical average of the past 10 years at the same location and during the same reference period. During the most water-critical phenological stages, such as vegetative development and tuber enlargement, the crop reported high water demands.
Chart 2. Soil moisture data taken by the sensor during the season in the experimental plots where the different irrigation strategies were applied. For each thesis and for each phenological stage, the total water percolated and the water in terms of irrigations and irrigation advice returned by the DSS is reported in mm
Far below average natural inputs and very high average temperatures have led to the need to increase the number of irrigation interventions.
Graph 2. shows the trend of the readings taken by the soil moisture sensors in the various theses. Also shown are data on percolation (sum of percolated water by phenological stage) and the specific irrigation advice for each thesis (total water in mm simulated and then carried out).
At the end of the season, close to harvest, a series of plant and plot surveys were conducted, including total production for each thesis. Some of the end-of-season data collected in the field and resulting from preliminary analysis are shown in the following table.
From an initial end-of-season analysis, it can be seen that the Thesis 2 carried out with the help of DSS irrigation from xFarm Technologies and characterized by an average return of 81% of Etc, resulted in an increase in production of about 80 Qli/ha almost for the same amount of water used for irrigation compared to the Traditional plot.
This difference, to be evaluated and validated during the 2023 experimental season, can be attributed to the increase in irrigation efficiency made possible by the integration of information from the irrigation DSS and the experience of technicians. The irrigation advice returned by the DSS made it possible to identify and quantify when to irrigate and how much water to give with respect to the actual needs of the plant at different phenological stages.
In Thesis 1, however, production was lower than in the other two theses because an average Etc return of 60 percent was chosen versus 81 percent and 78 percent, respectively. This led the crop to water stress that affected production.
As the trial has shown, DSS are an important ally for efficient water use. Despite the encouraging results of the field trial, however, further refinement of the models is needed so as to increase their effectiveness.
Therefore, in the 2023 season the behavior of the crop by subjecting it to "controlled water stresses" will be evaluated again using sensoristics and mathematical models. This will be critical to support the calibration and validation activities of the Irrigation DSS as it allows the identification of the optimal minimum and maximum soil moisture thresholds for the specific variety, phenological stage, soil texture and agro-environmental characteristics.
The goal is, for the same output, to increase water use efficiency.
This will be possible not only by setting up field experiments thanks to which contextualized data is collected on the crop, areal and soil/climatic characteristics that will allow the product to be "customized" to the farm and crop needs, but especially thanks to Decision Support Systems such as DSS and the use of innovative technologies that will support and improve decision-making by the technician-farmer. All made available through the xFarm platform with the ongoing support of the research and development team that will accompany this and many other agronomic projects.
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