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In the Mediterranean area, Greece is considered as one of the most important olive producing countries. Specifically, olive (Olea europaea) is the main crop covering more than 75% of the total tree cultivation area, while about 45% of the total cultivated area is being irrigated. Groundwater is the main source of irrigated water in Greece, but the growing demand and the climate change are putting this resource in risk. The design of an optimal irrigation plan, based on detailed measurements and modeling tools, can effectively contribute towards water saving with no loss on crop yield, in the area. In this study, an IoT-based decision-making system for the management of irrigation water resources of olive orchards in a small sub-basin of Lasithi, Crete, Greece is presented. The system integrates monitoring of soil moisture and atmospheric parameters in four fields within the study area and modeling approaches, using the modules of MIKE-SHE model, to simulate water flow in the unsaturated zone at the sub-basin level. Additionally, 45 soil samples (from 3 different soil depths) have been collected and analyzed from the study area for soil texture, bulk density, rock percentage, pH, organic matter, and mineral nutrients. After the successful calibration of the model (comparison of simulated against observed soil moisture values) the spatio-temporal representation of soil moisture is used as guidance for developing optimal irrigation scheduling, considering olive tree water requirements, for the entire study area (sub-basin), even for olive orchards with lack of monitoring equipment installation.ACKNOWLEDGEMENTSThe authors acknowledge contribution of the AgriCapture CO2 - Horizon 2020 project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101004282

The major global challenge for the coming decades will be increasing crop production with less water consumption. Precision agriculture (PA) and variable deficit irrigation (VDI) are management strategies that help farmers to improve crop production, fertilizer’s efficiency, and water use efficiency (WUE). The effects of irrigation (IR1 = variable deficit irrigation; IR2 = farmers’ irrigation common practices) under three fertilization (Ft1, Ft2, Ft3) treatments were studied on a cotton yield, on various indicators for more efficient water and fertilizer use, and on plant growth characteristics by applying a number of new agrotechnologies (such as TDR sensors; soil moisture (SM); PA; remote-sensing NDVI (Sentinel-2 satellite sensors); soil hydraulic analyses; geostatistical models; and SM root-zone modelling 2D GIS mapping). The reference evapotranspiration was computed based on the F.A.O. Penman–Monteith method. The crop (ETc) and actual (ETa) evapotranspiration were computed using crop coefficients obtained from the remote-sensing NDVI vegetation index (R2 = 0.9327). A daily soil–water–crop–atmosphere (SWCA) balance model and a depletion model were developed using sensor data (climatic parameters’ sensors, as well as soil and satellite sensors) measurements. The two-way ANOVA statistical analysis results revealed that irrigation (IR1 = best) and fertilization treatments (Ft2 = best) significantly affected the cotton yield, the plant height, the plant stem, the boll weight, the above-ground dry matter, nitrogen and fertilizer efficiency, and WUE. VDI, if applied wisely during critical growth stages, could result in a substantial improvement in the yield (up to +28.664%) and water savings (up to 24.941%), thus raising water productivity (+35.715% up to 42.659%), WUE (from farmers’ 0.421–0.496 kg·m−3 up to a VDI of 0.601–0.685 kg·m−3), nitrogen efficiency (+16.888% up to +22.859%), and N-P-K fertilizer productivity (from farmers’ 16.754–23.769 up to a VDI of 20.583–27.957).

Despite the fact that the olive tree is one of the best-adapted species in Mediterranean hydroclimate conditions, climate extremes impose negative effects on olive fruit set and development and subsequently on crop yield. Considering that the frequency of climate extremes is increasing in the last years due to climate change, Good Agricultural Practices (GAPs) have to be applied in order to mitigate their impact on olive trees. In this context, 18 experimental olive groves (irrigated and rainfed) were established, located on the island of Crete (south Greece). A set of 13 GAPs were applied in different combinations, mainly targeting to reduce water losses and erosion, alleviate heat stress and increase water use efficiency. Each experimental orchard was divided into two parts, the control (business-as-usual) and experimental (GAPs implementation). Four indicators were used for the assessment of GAPs performance, namely, Water Productivity (WP), Economic Water Productivity (EWP), Runoff (RF), and Yield (Y). WP and EWP were found to be up to 2.02 and 2.20 times higher, respectively, in the demonstration part of the orchards compared to the control, while Y was found to be up to 119% higher. RF was higher up to 190% in the control compared to the demonstration part of the experimental orchards. The above results clearly demonstrate that the implementation of the proposed GAPs can significantly support the adaptation of olive crops to extreme conditions.

Sustainable agriculture management typically requires detailed characterization of physical, chemical, and biological aspects of soil properties. These properties are essential for agriculture and should be determined before any decision for crop type selection and cultivation practices. Moreover, the implementation of soil characterization at the beginning could avoid unsustainable soil management that might lead to gradual soil degradation. This is the only way to develop appropriate agricultural practices that will ensure the necessary soil treatment in an accurate and targeted way. Remote sensing and geophysical surveys have great opportunities to characterize agronomic soil attributes non-invasively and efficiently from point to field scale. Remote sensing can provide information about the soil surface (or even a few centimeters below), while near-surface geophysics can characterize the subsoil. Results from the methods mentioned above can be used as an input model for soil and/or soil/water interaction modeling. The soil modeling can offer a better explanation of complex physicochemical processes in the vadose zone. Considering their potential to support sustainable agriculture in the future, this paper aims to explore different methods and approaches, such as the applications of remote sensing, geophysics, and modeling in soil studies.

This work investigates the impact of municipal solid waste compost (MSW-compost) application (0, 50, and 100 t/ha) on the growth, and on nutrient and trace elements content in lettuce and tomato plants grown in large, 40-L pots. Our findings showed inhibition of plants’ growth with increasing dose of MSW-compost, compared to plants receiving conventional fertilization. Growth inhibition was associated with a sharp decrease in soil NO3–N content. On the other hand, a slower decrease in soil NO3–N content occurred in non-planted pots amended with MSW-compost. These findings provide evidence that N immobilization and/or decreased N mineralization were responsible for inhibited growth by constraining N availability. With regard to the other macro-nutrients, K, P, Mg, Ca, and Fe, their contents in leaves of both crops were maintained at optimum levels. Higher zinc and copper content was measured in leaves of both crops but they did not exceed the optimum range for growth. No accumulation of trace eleme...