# Prediction of Almond Nut Yield and Its Greenhouse Gases Emission Using Different Methodologies
## 1 Introduction  
Today, production in modern agricultural systems is directly affected by energy inputs [1]. Every parts in the industrial food system depends on consuming fossil fuels, from fertilizers production, to the processing and transporting of food products to market [2]. In general, oil and petroleum products comprise the main portion of the total energy consumption of the agricultural sector [3]. Energy production and consumption negatively impact the environment, mainly through the emission of greenhouse gases (GHGs) and air pollutants [4]. Therefore, as an imperative step towards sustainable agriculture and food production, energy sources must be used more efficiently [5]. A simple energy input-output evaluation is usually practiced to determine the efficiency of energy consumption in agricultural production systems. Numerous studies have performed this procedure on various crops, such as apples [6], grapes [7], cherries [8], rice [9], potatoes [10], and tangerines [11]. Farmers, insurers, and governments could effectively manage the risk of production by employing an accurate prediction method. Based on reviewing previous studies ofcrop yield prediction, several studies have been carried out on the topic using different techniques. [PERSON] et al. [12] studied a new approach for forecasting crop yield using the dynamic factor technique. The dynamic forecasting technique was developed for wheat yield by [PERSON] et al. [13] by successfully using a product simulation method combined with two regression-based models. [PERSON] et al. [14] developed an integrated model to predict greenhouse tomato yield concerning the sensitivity assessment. Practicing machine learning for predicting almond yield was successfully conducted using some input variables such as grower data, weather data, and remote sensing imagery [15]. The variation in almond yields was determined by using a machine learning method based on a 10-year dataset of field light interception and meteorological data [16]. The modeling and prediction of agricultural production according to energy consumption patterns is an exciting topic for scientists [17]. In a production process, artificial intelligence (AI) is more attractive compared to mathematical models [18]. The advantage of AI over the regular statistical methods has been discussed in several studies, such as [PERSON] et al. [19] for corn and soybean yield estimation, [PERSON] et al. [20] for economical evaluation of potato yield, [PERSON] and [PERSON] [17] for prediction and simulation of consumed energy for wheat production, [PERSON] and [PERSON] [21] for the prediction of tractor fuel consumption, [PERSON] et al. [18] for the prediction of backbreak in open pit blasting, and [PERSON] and [PERSON] [22] for broiler production. Artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS), as the two main artificial intelligence modeling techniques, are widely applied to model complex systems due to their being less time-consuming and having greater efficiency [23]. ANN is a powerful method for better and faster approaches to tackling complex problems. For each problem, ANN must be trained by using its own examples. In addition, the optimization process regularly finishes at an optimized state since ANN has a local minima problem. However, the relationships could be easily predicted by a correctly trained ANN even for a higher number of variables [24]. Moreover, ANN is highly effective for tasks with minimal data sets, inadequate or complex input data. ANNs have been practiced for the simulation of various complex problems, such as the hot air-infrared drying process of rough rice [25], energy consumption based on economic and demographic variables [26], grape production regarding the input energies [1], prediction of ammonia emissions from livestock manure [27], and prediction of consumed energy and emitted greenhouse gases (GHGs) in apple production [28].