Smart farming based on Internet of Things (IoT) technologies enables crop farmers to collect real-time data related to irrigation and plant protection processes, aiming to increase production volume, improve product quality, and predict diseases, while optimizing resources and farming processes. IoT devices can collect vast amounts of environmental, soil, and crop performance data, thus building time series data that can be analyzed to forecast and compute recommendations and deliver critical information to farmers in real time. In this sense, the added-value from the farmers’ perspective is that such smart farming techniques have the potential to deliver a more sustainable agricultural production, based on a more precise and resource-efficient approach in the complex and versatile agricultural environment. The aim of this study is to investigate possible advantages of applying the Smart Farming as a Service (SFaaS) paradigm, aiming to support small-scale farmers, by taking over the technological investment burden and offering next generation farming advice through the combined utilization of heterogeneous information sources. The overall results of the pilot application demonstrate a potential reduction of up to 22% on total irrigation needs and important optimization opportunities on pesticides use efficiency. The current work offers opportunities for innovation targeting and climate change adaptation options (new agricultural technologies), and could help farmers to reduce their ecological footprint.

This study aims to evaluate critical success factors (CSFs), considering the perspectives of various stakeholders who are involved in enterprise resource planning (ERP) implementation in agricultural processing companies in Central Macedonia (Greece). This evaluation—combined with aspects from the literature—may show how a digital production system can be managed and redesigned to become sustainable for a company. Research was conducted through the use of a specially designed questionnaire that was addressed to various stakeholders in the ERP implementation in agricultural processing companies. Descriptive statistics, Grey Relational Analysis (GRA), and Friedman test methods were used in order for relevant information to be identified and valid conclusions to be drawn. Given the results of the grey relational analysis (GRA), respondents consider 33 out of 37 critical success factors to be “very important”. Friedman test results seem to differ from GRA results in 19 out of 37 factors with regard to their ranking. Based on this, and using two different methods, the most important ERP critical success factors are highlighted. For the objective aim of this paper to be fulfilled, there is a contribution giving insights into stakeholders’ perspectives regarding the management of a digital production system in the field of agricultural processing companies. It also contributes to the literature, as it highlights methods that refer to the evaluation of critical success factors for ERP implementation.

Agriculture is highly dependent on climate change, and Cyprus especially is experiencing its impacts on agricultural production to a greater extent, mainly due to its geographical location. The adaptation of farming to the effects of global climate change may lead to the maximization of agricultural production, which is an important and desirable improvement. The main aim of this paper is to rank and quantify the impacts of climate change on the agricultural sector of Cyprus, through a multi-round Delphi survey seeking a consensus agreement in a group of experts. A multidisciplinary group of 20 experts stated their willingness-to-pay for various impacts of climate change. By applying this method, the individual impacts of climate change on crop production and water resources were brought into the modeling effort on equal footing with cost values. The final cost impact estimate represents the total estimated cost of climate change in the agricultural sector. According to the results, this cost reaches EUR 25.08 million annually for the agricultural sector, and EUR 366.48 million for the whole country. Therefore, it is expected that in the seven-year programming period 2014–2020 the total cost of climate change on agriculture ranges from EUR 176 to EUR 2565 million. The most significant impacts are due to the increasing level of CO₂ in the atmosphere and the burden of biodiversity and ecosystems.

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.

Focusing on sustainability, the new Common Agricultural Policy (2023–2027) sets ambitious goals for water management, as reducing irrigation water use is a vital issue. Cooperation among farmers, relevant authorities, and researchers plays a significant role in achieving these objectives. Therefore, this study applies a multicriteria mathematical programming model to optimize land use, considering water use, profit, labor, and cost. The model was applied to three farmer groups located in Greece and proved to be valuable in the implementation of irrigation water use. Using the same methodology, two additional cases of farmer groups that utilize drylands are presented in complementary ways to investigate how the new CAP affects non-irrigated land uses. Regarding the irrigated case, reducing water usage involves decreasing the land dedicated to crops characterized by high water demand, such as rice, corn, vetch, and clover. This adjustment stems from the necessity to replace irrigated land with non-irrigated land because climate change demands low water consumption for crops and underscores the importance of the new policy framework to promote sustainable agriculture. As for the non-irrigated case, achieving optimal farm planning entails reducing the cultivated areas of vetch, grassland, and sunflower. This result is driven by the need to increase crops receiving primary subsidies, highlighting the necessity for non-irrigated farms to enhance their profitability through the benefits provided by the Common Agricultural Policy. Lastly, it is important to note that this study significantly contributes to guiding decision-makers in achieving alternative agricultural land uses and farm plans while also aiding in the comprehension of the new cross-compliance rules.

Technical efficiency is considered a useful advisory tool for managers whose main goal is to maximize profit and minimize costs. Data envelopment analysis is a widely accepted methodology for technical efficiency estimation in the sector of agriculture. For that reason and with the view to extract useful conclusions regarding farmers’ effective management of inputs, this study aims to present the DEA method through its implementation in a set of farms located in the regional unit of Pieria. To conduct this analysis, relevant data were collected through a survey in which 40 farms participated. The output variable was chosen to be each farm’s total amount of sales, while the inputs were selected in a way to represent the main factors of production, such as (1) land in acres, (2) labor in hours, and (3) variable costs in EUR. The results showed that the examined farms need to reduce the inputs used by 34.6% to operate more efficiently from the point of view of the CRS model. Therefore, farmers should be motivated to reduce the inputs used, something that can be done through the provision of specialized advisory services. This will, of course, be helped by both the local authorities and the policies of the country in which the rational use of inputs seems to be necessary. This study may contribute to the relevant literature, agriculture, and the area since management suggestions are formulated for the farmers of Pieria’s regional unit.