The HADES Yield Prediction System – A Case Study on the Turkish Hazelnut Sector

Abstract

Crop yield forecasting activities are essential to support decision making of farmers, private companies and public entities. While standard systems use georeferenced agro-climatic data as input to process-based simulation models, new trends entail the application of machine learning for yield prediction. In this paper we present HADES (HAzelnut yielD forEcaSt), a hazelnut yield prediction system, in which process-based modeling and machine learning techniques are hybridized and applied in Turkey. Official yields in the top hazelnut producing municipalities in 2004–2019 are used as reference data, whereas ground observations of phenology and weather data represent the main HADES inputs. A statistical analysis allows inferring the occurrence and magnitude of biennial bearing in official yields and is used to aid the calibration of a process-based hazelnut simulation model. Then, a Random Forest algorithm is deployed in regression mode using the outputs of the process-based model as predictors, together with information on hazelnut varieties, the presence of alternate bearing in the yield series, and agro-meteorological indicators. HADES predictive ability in calibration and validation was balanced, with relative root mean square error below 20%, and R2 and Nash-Sutcliffe modeling efficiency above 0.7 considering all municipalities together. HADES paves the way for a next-generation yield prediction system, to deliver timely and robust information and enhance the sustainability of the hazelnut sector across the globe.

Rotten Hazelnuts Prediction via Simulation Modeling—A Case Study on the Turkish Hazelnut Sector

“…Model simulations start on October 1, defined as the starting date of the hazelnut growing season. The simulation of the hazelnut reproductive phases is performed according to Bregaglio et al (2016 , 2020 , 2021) , this special issue. Eight phenological phases were simulated for female reproductive development, from flowering to nut dropping.…”

Computational Models in Precision Fruit Growing: Reviewing the Impact of Temporal Variability on Perennial Crop Yield Assessment

“…In brief, the input variables most used for mechanistic modeling to yield forecasting were climatic indicators focused on summarized data for a given area, for example, daily or monthly averages (Bai et al, 2020;Bai et al, 2021;Maselli et al, 2012). It frequently used also speci c data from the orchards, such as physiological characteristics and vegetation indexes synthesized in indicators of phenological stages (Valdés-Gómez et al, 2009;Bregaglio et al ., 2021). In addition to these variables, the authors commonly count on topographical information and model-speci c calibration parameters (Cola et al, 2014).…”

Meteoroloji ve Tarım İlacı Kullanım Verilerinin Makine Öğrenmesi Yöntemlerine Entegre Edilmesi Yoluyla Tarımsal Üretim Tahmini: Türkiye’deki Başlıca Mahsuller İçin Bir Uygulama

“…According to the results, accurate estimations were obtained. Bregaglio et al (2021) presented a yield prediction system for hazelnut called HADES (HAzelnut yielD forEcaSt) that integrates ML techniques and process-based modelling. Ground observation and meteorological data between 2004-2019 are used along with the hazelnut yield figures.…”