Simulation of the water balance in cropping systems is an essential tool, not only to monitor water status and determine drought but also to find ways in which soil water and irrigation water can be used more efficiently. However, besides the requirement that models are physically correct, the spatial representativeness of input data and, in particular, accurate precipitation data remain a challenge. In recent years, satellite-based soil moisture products have become an important data source for soil wetness information at various spatial-temporal scales. Four different study areas in the Czech Republic and Austria were selected representing Central European soil and climatic conditions. The performance of soil water content outputs from two different crop-water balance models and the Metop Advanced SCATterometer (ASCAT) soil moisture product was tested with field measurements from 2007 to 2011. The model output for soil water content shows that the crop model Decision Support System for Agrotechnology Transfer performs well during dry periods (<30% plant available soil moisture (ASM), whereas the soil water-balance model SoilClim presents the best results in humid months (>60% ASM). Moreover, the model performance is best in the early growing season and decreases later in the season due to biases in simulated crop-related above-ground biomass compared with the relatively stable grass canopy of the measurement sites. The Metop ASCAT soil moisture product, which presents a spatial average of soil surface moisture, shows the best performance under medium soil wetness conditions (30–50% ASM), which is related to low variation in precipitation frequency and under conditions of low-surface biomass (early vegetation season).

Remote sensing can efficiently support the quantification of crop water requirements included in the goal of assessing water footprints, which is to analyse how human activities or specific products relate to issues of water scarcity and pollution and identify how activities and products can become more sustainable from a water perspective. Remote sensing techniques have become popular in estimating actual crop evapotranspiration and hence crop water requirements in recent decades due to the advantages they offer to users, e.g. low cost, regional data and use of maps instead of point measurements as well as saving time. The use of earth observation data supports models’ accuracy in the procedure for assessing water footprint, since no average values are used: instead, users have real values for the specific parameters.

The present study provides two examples of how remote sensing techniques are used essentially for estimating evapotranspiration along with crop yield, two basic parameters, for assessing water footprint. Two different case studies have been illustrated to define the methodology proposed, which refers to Mediterranean conditions and can be applied after inferring the necessary field data of each crop. The first case study refers to the application of Surface Energy Balance Algorithm for Land (SEBAL) for estimating evapotranspiration, while the second refers to the Crop Yield prediction. Both elements, such as evapotranspiration and crop yield, are vital for water footprint accounting. Firstly, the SEBAL was adopted, under the essential adaptations for local soil and meteorological conditions for estimating groundnut water requirements. Landsat-5 TM, Landsat-7 Enhanced Thematic Mapper+ and Landsat 8 OLI images were used to retrieve the required spectral data. The SEBAL model is enhanced with empirical equations regarding crop canopy factors, in order to increase the accuracy of crop evapotranspiration estimation. Maps were created for evapotranspiration (ET) using the SEBAL modified model for the area of interest. The results were compared with measurements from an evaporation pan, used as a reference. Statistical comparisons showed that the modified SEBAL can predict ETc in a very effective and accurate way and provide water footprint modellers with high-level crop water data. Yield prediction plays a vital role in calculating water footprint. Having real values rather than taking reference (or averaged) values from FAO is an advantage that Earth Observation means can provide. This is very important in econometric or any other prediction models used for estimating water footprint because using average data reduces accuracy. In this context, crop and soil parameters along with remotely sensed data can be used to develop models that can provide users with accurate yield estimations. In a second step, crop and soil parameters along with the normalized difference vegetation index were correlated to examine whether crop yield can be predicted and to define the actual time-window to predict the yield. Statistical and remote sensing techniques were then applied to derive and map a model that can predict crop yield. The algorithm developed for this purpose indicates that remote sensing observations can predict crop yields effectively and accurately. Using the statistical Student's t test, it was found that there was no statistically significant difference between predicted and real values for crop yield.

In the past 20 years, extreme weather events (atypical variations of temperature and precipitation in space and time) have increased in Bosnia and Herzegovina, the consequences being significant material damage and decreased crop yields. The aims of the current paper were to investigate the impact of climate change on yield, irrigation requirements and water productivity of maize grown in one of the most important agricultural regions of the country. It used the results of projections of the EBU-POM regional climate model (Eta Belgrade University – Princeton Ocean Model) for the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B, A2 and A1B > carbon dioxide (CO2), A2 > CO2 for the 2020s (2010–2039), the 2050s (2040–2069) and the 2080s (2070–2099), and it compared them with the reference period (1961–1990) using measured and modelled data. AquaCrop was calibrated for the study area using data from experimental studies. The model was applied in simulations of future cultivation scenarios considering different irrigation regimes and variations in precipitation, temperature, CO2 concentration and sowing date. The results of simulations indicate that in the 2020s, there will be no significant changes in irrigation needs and yield due to the earlier sowing date and overall shifting of the growing season early in spring. A slightly larger negative impact of climate change will occur by the 2050s and a larger one by the 2080s due to a reduction in precipitation in the summer months. Compared with the reference period, in the 2080s, irrigation requirements are expected to increase by almost 100% (from 100 to 200 mm) and to provide up to 30% greater yield. In the future, water productivity of maize will remain high and will be even greater than current levels for both rainfed and irrigated cultivation due to anticipation and shortening of the growing season, reduction in crop evapotranspiration and increase in CO2 concentration. Due to an increase in precipitation in early spring and its reduction in the May–June period, the blue-to-green water ratio will increase in the future with positive environmental connotations. The productive use of blue water will also increase.

In the current study, simulations by five crop models (WOFOST, CERES-Barley, HERMES, DAISY and AQUACROP) were compared for 7–12 growing seasons of spring barley (Hordeum vulgare) at three sites in the Czech Republic. The aims were to compare how various process-based crop models with different calculation approaches simulate different values of transpiration (Ta) and evapotranspiration (ET) based on the same input data and compare the outputs of these simulations with reference data. From the outputs of each model, the water use efficiency (WUE) from Ta (WUETa) and from actual ET (WUEETa) was calculated for grain yields and above-ground biomass yield. The results of the first part of the study show that the model with the Penman approach for calculating ET simulates lower actual ET (ETa) sums, at an average of 250 mm during the growing season, than other models, which use the Penman–Monteith approach and simulate 330 mm on average during the growing season. In the second part of the current study, WUE reference values in the range 1.9–2.4 kg/m3 were calculated for spring barley and grain yield. Values of WUETa/WUEETa calculated from the outputs of individual models for grain yields and above-ground biomass yields ranged from 2.0/1.0 to 5.9/3.8 kg/m3 with an average value of 3.2/2.0 kg/m3 and from 3.9/2.1 to 10.5/6.8 kg/m3 with an average value of 6.5/4.0 kg/m3, respectively. The results confirm that the average values of all models are nearest to actual values.

A probabilistic crop forecast based on ensembles of crop model output (CMO) estimates offers a myriad of possible realizations and probabilistic forecasts of green water components (precipitation and evapotranspiration), crop yields and green water footprints (GWFs) on monthly or seasonal scales. The present paper presents part of the results of an ongoing study related to the application of ensemble forecasting concepts for agricultural production. The methodology used to produce the ensemble CMO using the ensemble seasonal weather forecasts as the crop model input meteorological data without the perturbation of initial soil or crop conditions is presented and tested for accuracy, as are its results. The selected case study is for winter wheat growth in Austria and Serbia during the 2006–2014 period modelled with the SIRIUS crop model. The historical seasonal forecasts for a 6-month period (1 March-31 August) were collected for the period 2006–2014 and were assimilated from the European Centre for Medium-range Weather Forecast and the Meteorological Archival and Retrieval System. The seasonal ensemble forecasting results obtained for winter wheat phenology dynamics, yield and GWF showed a narrow range of estimates. These results indicate that the use of seasonal weather forecasting in agriculture and its applications for probabilistic crop forecasting can optimize field operations (e.g., soil cultivation, plant protection, fertilizing, irrigation) and takes advantage of the predictions of crop development and yield a few weeks or months in advance.

A probabilistic crop forecast based on ensembles of crop model output estimates, presented here, offers an ensemble of possible realizations and probabilistic forecasts of green water components, crop yield and green water footprints (WFs) on seasonal scales for selected summer crops. The present paper presents results of an ongoing study related to the application of ensemble forecasting concepts in crop production. Seasonal forecasting of crop water use indicators (evapotranspiration (ET), water productivity, green WF) and yield of rainfed summer crops (maize, spring barley and sunflower), was performed using the AquaCrop model and ensemble weather forecast, provided by The European Centre for Medium-range Weather Forecast. The ensemble of estimates obtained was tested with observation-based simulations to assess the ability of seasonal weather forecasts to ensure that accuracy of the simulation results was the same as for those obtained using observed weather data. Best results are obtained for ensemble forecast for yield, ET, water productivity and green WF for sunflower in Novi Sad (Serbia) and maize in Groß-Enzersdorf (Austria) – average root mean square error (2006–2014) was <10% of observation-based values of selected variables. For variables yielding a probability distribution, capacity to reflect the distribution from which their outcomes will be drawn was tested using an Ignorance score. Average Ignorance score, for all locations, crops and variables varied from 1.49 (spring barley ET in Groß-Enzersdorf) to 3.35 (sunflower water productivity in Groß-Enzersdorf).

Saving water in irrigated agriculture is a high priority in areas with scarce water resources and impacted by climate change. This paper presents results of measurements on water productivity (WP) under alternative rice growing practices such as alternating wetting and drying, direct seeded rice, modified systems of rice intensification and conventional paddy rice (NI) in two selected districts (Guntur in Andhra Pradesh and Nalgonda in Telangana, India). Under alternative practices, the yields varied from 5.72 to 6.11 t/ha compared with 4.71 t/ha under paddy rice. The average water application varied from 991 to 1494 mm under alternative practices while average application in conventional paddy rice was 2242 mm. Higher yield and lower water application led to an increase in WP varying from 0.45 to 0.59 kg/m3 under alternative practices compared with 0.22 kg/m3 under conventional paddy rice. The measurements showed that less water can be used to produce more crop under alternative rice growing practices. The results are important for water-scarce areas, providing useful information to policy makers, farmers, agricultural departments and water management boards in devising future climate-smart adaptation and mitigation strategies.

Drought represents one of the major constraints on agricultural productivity and food security and in future is destined to spread widely as a consequence of climate change. Research efforts are focused on developing strategies to make crops more resilient and to mitigate the effects of stress on crop production. In this context, the use of root-associated microbial communities and chemical priming strategies able to improve plant tolerance to abiotic stresses, including drought, have attracted increasing attention in recent years. The current review offers an overview of recent research aimed at verifying the role of arbuscular mycorrhizal fungi and chemical agents to improve plant tolerance to drought and to highlight the mechanisms involved in this improvement. Attention will be devoted mainly to current knowledge on the mechanisms involved in water transport.

In the current regional-scale study, the model DSSAT CROPGRO was applied in order to simulate the cultivation of industrial tomato and to estimate the green water (GW), blue water (BW), blue water requirement (BWR) and water footprint (WFP) through a dual-step approach (with and without full irrigation). Simulation covered a period of 30 years for three climate scenarios including a reference period and two future scenarios based on forecast global average temperature increases of 2 and 5 °C. The spatial patterns of indicators relating to the whole territory of Puglia region (Southern Italy), characterized by the high evaporative demand of the atmosphere, are discussed and analysed. Considering the climatic pattern, the analysis was developed for three areas (Northern, Central and Southern). Future scenarios affected all indicators significantly, particularly the Northern area, characterized by higher temperature and rainfall anomalies. Under the A5 scenario, compared with the baseline, this area was forecast to have a large increase of BW (+30%) and reduction in yield (−20%). As a consequence, the BWR and WFP were predicted to increase dramatically, up to 40 and >65%, respectively. On the other hand, Central and Southern areas, with lower anomalies of temperature and rainfall, were forecast to be less vulnerable to climate change. The distributed analysis performed could be important for water policy, allowing most efficient allocation of scarce water resources and concentrating them where the WFP is lowest, or in other words, water use efficiency is highest.

Irrigation according to reliable estimates of crop water requirements (CWR) is one of the key strategies to ensure long-term sustainability of irrigated agriculture. In southern Mediterranean regions, during the irrigation season, CWR is almost totally controlled by the potential evapotranspiration of the irrigated crop. An innovative system for forecasting crop potential evapotranspiration (ETp) has been implemented recently in the Campania region (southern Italy). The system produces ETp forecasts with a lead time of up to 5 days, by coupling the visible and near-infrared crop imagery with numerical weather prediction outputs of a limited area model. The forecasts are delivered to farmers with a simple and intuitive web app interface, which makes daily real-time ETp maps accessible from desktop computers, tablets and smartphones. Forecast performances were evaluated for maize fields of two farms in two irrigation seasons (2014–2015). The mean absolute bias of the forecasted ETp was <0.3 mm/day and the RMSE was <0.6 mm/day, both for lead times up to 5 days.

In the current study, for the main crops cultivated in the Campania region (South of Italy), three indicators were proposed and analysed. The blue water footprint (WFb), which gives an indication of the impact of irrigation on the water resource; the gross margin WFb (GMWFb), describing the economic productivity of irrigation; and the job WFb (JWFb) that expresses the social value of blue water in terms of job opportunities. Results confirmed that water applied through irrigation is much higher compared with crop requirements. In terms of GMWFb, silage maize, maize and alfalfa had the highest values, while olive, potato and tomato had the lowest. Concerning JWFb, silage maize was the crop with the highest value. Even though a deeper analysis should be done in terms of added value in the entire supply chain, the results indicated a clear difference between the crops related to animal feeding (alfalfa, maize) and the other crops taken into consideration. In fact, for the former, both the GMWFb and the JWFb achieved their highest values. Results showed that for certain irrigation volumes and for certain crops, the economic and social impacts are very different and the choice of an irrigated crop rather than another has different repercussions in terms of environmental and socio-economic sustainability. The proposed indicators would allow water managers and farmers to assess and compare production systems in terms of the different benefits that their use of water can provide.

The aim of the current study was to define, optimize and customize IRRISAT, a fully operative satellite-based irrigation advisory service (IAS) provided in Campania Region, Italy, using a choice experiment to determine the preferences of farmers regarding the main characteristics and attributes of IRRISAT. Furthermore, willingness to pay for the main attributes of the services provided was estimated. The information on the amount of water required for irrigation provided by the IAS is sent out to farmers via SMS and email, as well as via the IRRISAT webpage. The study was related to the 2013–2014 irrigation season, when the service provided support to 669 farmers over an area of 55 ha. The study considered four attributes and levels of IRRISAT service: land management unit (scale of service); different levels of water saving (5, 10 and 30%) that could be achieved at different prices; annual fee paid by farmers (ranging between €6 and €10/ha/year); length of contract for the service supply, ranging from 1 to 3 years. Results showed that farmers’ preferences are influenced positively by scale (entire area of the farm instead of single fields) and duration of the service delivering contract. Concerning the duration of the contract, the most preferred option was the 3-year service. Finally, water saving was shown to affect farmers’ choices very little and thus it is probably less attractive for farmers probably due to the low price and to a relatively large availability of water for irrigation.