Short-Term Forecasting of Crop Production for Sustainable Agriculture in a Changing Climate

first_page settings Order Article Reprints Open AccessArticle Short-Term Forecasting of Crop Production for Sustainable Agriculture in a Changing Climate by Vincenzo Guerriero 1,* [ORCID] , Anna Rita Scorzini 1 [ORCID] , Bruno Di Lena 2, Mario Di Bacco 3 [ORCID] and Marco Tallini 1 [ORCID] 1 Department of Civil, Environmental and Architectural Engineering, University of L’Aquila, 67100 L’Aquila, Italy 2 Abruzzo Region, Agriculture Department, 66054 Vasto, Italy 3 Department of Civil and Environmental Engineering, University of Florence, 50139 Florence, Italy * Author to whom correspondence should be addressed. Sustainability 2025, 17(13), 6135; https://doi.org/10.3390/su17136135 Submission received: 14 May 2025 / Revised: 19 June 2025 / Accepted: 1 July 2025 / Published: 4 July 2025 (This article belongs to the Special Issue Impact of Climate Change and Extreme Weather Events on Crop Growth, Pest Management and Crop Production) Download keyboard_arrow_down Browse Figures Versions Notes Abstract Globally, crop productive systems exhibit climatic adaptation, resulting in increased overall yields over the past century. Nevertheless, inter-annual fluctuations in production can lead to food price volatility, raising concerns about food security. Within this framework, short-term crop yield predictions informed by climate observations may significantly contribute to sustainable agricultural development. In this study, we discuss the criteria for historical monitoring and forecasting of the productive system response to climatic fluctuations, both ordinary and extreme. Here, forecasting is intended as an assessment of the conditional probability distribution of crop yield, given the observed value of a key climatic index in an appropriately chosen month of the year. Wheat production in the Teramo province (central Italy) is adopted as a case study to illustrate the approach. To characterize climatic conditions, this study utilizes the Standardized Precipitation Evapotranspiration Index (SPEI) as a key indicator impacting wheat yield. Validation has been carried out by means of Monte Carlo simulations, confirming the effectiveness of the method. The main findings of this study show that the model describing the yield–SPEI relationship has time-varying parameters and that the study of their variation trend allows for an estimate of their current values. These results are of interest from a methodological point of view, as these methods can be adapted to various crop products across different geographical regions, offering a tool to anticipate production figures. This offers effective tools for informed decision-making in support of both agricultural and economic sustainability, with the additional benefit of helping to mitigate price volatility.