Creating Foliar Diagnosis Rules for Almond Trees (Prunus dulcis, Mill.) Grown Under Different Watering Regimes: Irrigation vs Rainfed

Abstract

Agriculture in the 21st Century must be performed considering sustainability criteria to mitigate the effects of climate change. For this, adequate fertilization is necessary for avoiding the excess application of fertilizers, which could contaminate the environment. For the efficient management of fertilization, it is necessary to know the optimum levels of each nutrient for each specie and type of environment. The most common method is to interpret foliar analyses results with traditional tools such as the Range of Normality (RN) or through more precise and complex techniques, such as the Diagnosis and Recommendation Integrated System (DRIS), or the Compositional Nutrient Diagnosis (CND). However, for almonds, little information is available on the nutritional requirements of the different varieties, and those cultivated in rainfed vs irrigated lands are not differentiated. In the present work, 384 samples from each of four almond varieties (Prunus dulcis, Mill.) Desmayo, Ramillete, Marcona and Tuono, grown in rainfed or irrigated lands (a total of 1,536 samples) were analyzed, corresponding to sampling every two weeks between the months of June and September, both months included, for a period of two consecutive years. The main objective of the work was to establish RN, DRIS and CND standards for the interpretation of the nutritional analysis of these four almond varieties grown under different watering regimes. With the data from mineral analysis, through the application of different mathematical and statistical models, the RN, DRIS, and CND standards were obtained, with the conclusion that the optimal period for sampling this crop was in the month of July. These standards could be useful for developing algorithms that could be utilized to develop decision support systems (DSS) that interpret the foliar analyses more precisely as compared to the simple RN, and which manage, based on this information, the fertilization of the crops.