The estimation of crop evapotranspiration (ETc) is crucial for irrigation water management, especially in arid regions. This can be particularly relevant in the Po Valley (Italy), where arable lands suffer from drought damages on an annual basis, causing drastic crop yield losses. This study presents a novel approach for vegetation-based estimation of crop evapotranspiration (ETc) for maize. Three years of high-resolution multispectral satellite (Sentinel-2)-based Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Leaf Area Index (LAI) time series data were used to derive crop coefficients of maize in nine plots at the Acqua Campus experimental farm of Irrigation Consortium for the Emilia Romagna Canal (CER), Italy. Since certain vegetation indices (VIs) (such as NDVI) have an exponential nature compared to the other indices, both linear and power regression models were evaluated to estimate the crop coefficient (K_c). In the context of linear regression, the correlations between Food and Agriculture Organization (FAO)-based K_c and NDWI, NDRE, NDVI, and LAI-based K_c were 0.833, 0.870, 0.886, and 0.771, respectively. Strong correlation values in the case of power regression (NDWI: 0.876, NDRE: 0.872, NDVI: 0.888, LAI: 0.746) indicated an alternative approach to provide crop coefficients for the vegetation period. The VI-based ETc values were calculated using reference evapotranspiration (ET₀) and VI-based K_c. The weather station data of CER were used to calculate ET₀ based on Penman-Monteith estimation. Out of the Vis, NDWI and NDVI-based ETc performed the best both in the cases of linear (NDWI RMSE: 0.43 ± 0.12; NDVI RMSE: 0.43 ± 0.095) and power (NDWI RMSE: 0.44 ± 0.116; NDVI RMSE: 0.44 ± 0.103) approaches. The findings affirm the efficacy of the developed methodology in accurately assessing the evapotranspiration rate. Consequently, it offers a more refined temporal estimation of water requirements for maize cultivation in the region.

Numerous Information and Communication Technologies (ICTs) applications have been developed in irrigated agriculture. While there are studies focusing on ICTs impacts at the farm level, no research deals with this issue at the Water Authority (WA) level where ICTs can support strategic decisions on land and water allocation. The present study aims to design a theoretical model to estimate economic benefits from the ICT-informed decision process of water management in agriculture. Specifically, the study analyzes the motivations driving a case study WA using ICTs to support strategic management decisions involving risky choices. Results show that the WA under investigation has potentialities to save water and to implement adaptation strategies to climate change. Higher benefits from ICTs are attainable in areas with limited water availability and where the WA can effectively manage land allocation and control water delivery volumes. The study concludes that ICTs might have a disruptive potential in fulfilling WA’s specific information needs, but there is still a need to improve their accuracy due to the risk surrounding the decisions at stake.

The irrigation water requirements of different crops are becoming a pivotal driver for the governance strategies of water allocation and management. This paper estimates the impact of irrigation water requirements on economic value in terms of the yields and gross saleable production of 13 different crops cultivated in the Emilia-Romagna region (Italy) over the 2010–2020 period by exploiting a generalized propensity score matching approach. Results show that the overall irrigation water requirements affect crops’ economic value. There is a causal effect of water irrigation on economic value: positive only for high levels of water irrigation in the case of yields, while it reverses and assumes a concave shape for gross saleable production. However, the effect is mediated by the irrigation water requirements of different crops. In water scarcity conditions, the allocation of water to arboreal crops, given the effect of water irrigation on gross saleable production, is also positive for small quantities of water. This paper can help guide the design of more sustainable water management strategies and agricultural development policies to face climate change.

Several studies address the topic of Information and Communication Technologies (ICT) adoption in irrigated agriculture. Many of these studies testify on the growing importance of ICT in influencing the evolution of the sector, especially by bringing down information barriers. While the potentialities of such technologies are widely investigated and confirmed, there is still a gap in understanding and modeling decisions on ICT information implementation. This gap concerns, in particular, accounting for all the aspects of uncertainty which are mainly due to a lack of knowledge on the reliability of ICT and on the errors of ICT-information. Overall, such uncertainties might affect Decision Makers’ (DM’s) behavior and hamper ICT uptake. To support policy makers in the designing of uncertainty-management policies for the achievement of the benefits of a digital irrigated agriculture, we further investigated the topic of uncertainty modelling in ICT uptake decisions. To do so, we reviewed the economic literature on ambiguity, in the context of the wider literature on decision making under uncertainty in order to explore its potential for better modeling ICT uptake decisions. Findings from the discussed literature confirm the capabilities of this approach to yield a deeper understanding of decision processes when the reliability of ICT is unknown and provides better insights on how behavioral barriers to the achievement of potential ICT-benefits can be overcome. Policy implications to accompany the sector in the digitalization process include mainly: (a) defining new approaches for ICT-developers to tailor platforms to answer heterogeneous DMs’ needs; (b) establish uncertainty-management policies complementary to DM tools adoption.

Subjective behavior of decision makers (DMs) is paramount when modeling information and communication technology (ICT) adoption choices in irrigated agriculture. Here, efficient ICT-aided irrigation plans often involve a certain degree of uncertainty, and differential attitudes toward it can cause uncoordinated actions between actors. Some DMs will implement ICT information, while others will not because they do not trust ICT reliability. This risks undermining the achievement of ICT benefits in terms of water saving at the irrigation district level. By distinguishing between different sources of uncertainty, taking the form of risk and ambiguity, in the present paper, we developed a new decision model to assess the impact that subjective behavior and learning processes have on the efficiency of ICT-aided irrigation plans. A case study was selected to implement the model in simplified settings. The results revealed the potential of ambiguity to limit ICT information implementation and to hinder water governance. Implications mainly concern the development of uncertainty management policies to favor DMs becoming familiar with the new ICT with lower ambiguity.