Prediction of Saffron Yield Based on Soil Properties as a Way to Identify Susceptible Lands of Saffron. Issue 20 (13th November 2021)

• Record Type: Journal Article
• Authors: Tashakkori, Fatemeh; Mohammadi Torkashvand, Ali; Ahmadi, Abbas; Esfandiari, Mehrdad
• Is Part Of: Communications in soil science and plant analysis. Volume 52:Issue 20(2021)
• Page Start: 2471
• Page End: 2481
• Publication Date: 2021-11-13
• Subjects: Saffron yield -- soil texture -- multilayer perceptron -- phosphorus -- Golestan; Soil science -- Periodicals; Plants -- Chemical analysis -- Periodicals; Agricultural chemistry -- Periodicals; 631.405
• DOI: 10.1080/00103624.2021.1929284
• Languages: English
• ISSNs: 0010-3624

Abstract

ABSTRACT: Saffron ( Crocus sativus L.) is one of the most important crops produced globally, and in only a limited number of countries. Determining the best conditions for cultivating this crop is important. Prediction of saffron yield according to soil characteristics can help to evaluate the land's ability to cultivate this valuable plant. To achieve this objective, 100 soil samples were taken. Physicochemical properties, such as soil texture, nutrients, soil acidity, electrical conductivity, organic matter and lime were measured. After harvesting saffron, fresh weight of the saffron flower was measured in kg ha −1 . Using artificial neural networks and creating different models with different data sets of soil properties as the input and saffron yield as the output, the ability of this network was evaluated in prediction of the saffron yield. Available phosphorus and organic matter based on the results and the Pearson coefficient are the most effective factors on saffron yield. Evaluation of the model results indicated that the coefficient varied was obtained from 0.45 to 0.89. The best model for saffron yield estimation was obtained when phosphorus, organic matter, potassium and electrical conductivity were used as the input, so that values of R 2 and root mean square error (RMSE) were obtained at 0.891 and 0.89 kg ha −1, respectively.