GIUSEPPE LOPRIORE

There is considerable unused and unproductive land in rural areas of the Puglia region, Southern Italy. These areas and their local cultivators/growers have always been overlooked by academic, policy, and investment circles despite their potential to improve food security and the livelihood of rural communities. Therefore, it is crucial to evaluate the sustainability potential of these areas to produce food, feed, and non-food products. This evaluation will play a vital role in the sustainable development of rural regions such as the Puglia region. In this review, we highlighted important aspects regarding the management potential and the expected contribution that various types of marginal areas (MAs) could add to the regional economy of Puglia. The authors focused on the extent to which Puglia’s marginal lands can realistically be utilized to meet Italy’s food production targets, considering the economic, social, and environmental potential of different marginal area types within the Puglia territories. In writing this review, we have been inspired by the lack of sufficient information necessary to carry out a plan for the revitalization of MAs and the sustainable development of regional rural areas. Although Italian scholars have not extensively researched MAs, the available data suggest that they could significantly contribute to the development of regional economy and food security, despite their complexity and low-input nature. The scientific evidence suggests that the main challenge associated with using MAs for food and/or non-food production is balancing biodiversity conservation with local residents’ social and economic development. Presently, some small-scale or family farms are already operating within MAs. However, they still contribute minimally to the overall agricultural production in the region, even though they have the potential to play an important economic role for numerous rural communities in Puglia. Furthermore, the available data indicate that over 40% of existing farmers in MAs require greater support to sustain their activities. Such support should be tailored to the local context, built on and, where appropriate, improved upon existing practices, while addressing various threats to these areas. In this regard, sustainable development policies should shape MAs’ landscapes through the support of initiatives aimed at the recovery of traditional agricultural activities, the use of local varieties, the protection of the environment, and the conservation of biodiversity. The authors believe that this review provides policymakers with recommendations to consider when supporting the sustainable use of MAs as a potential source of food security and sustainable development for people living in these areas.

Assessing plant water status accurately in both time and space is crucial for maintaining satisfactory crop yield and quality standards, especially in the face of a changing climate. Remote sensing technology offers a promising alternative to traditional in situ measurements for estimating stem water potential (Ψstem). In this study, we carried out field measurements of Ψstem in an irrigated olive orchard in southern Italy during the 2021 and 2022 seasons. Water status data were acquired at midday from 24 olive trees between June and October in both years. Reflectance data collected at the time of Ψstem measurements were utilized to calculate vegetation indices (VIs). Employing machine learning techniques, various prediction models were developed by considering VIs and spectral bands as predictors. Before the analyses, both datasets were randomly split into training and testing datasets. Our findings reveal that the random forest model outperformed other models, providing a more accurate prediction of olive water status (R2 = 0.78). This is the first study in the literature integrating remote sensing and machine learning techniques for the prediction of olive water status in order to improve olive orchard irrigation management, offering a practical solution for estimating Ψstem avoiding time-consuming and resource-intensive fieldwork.