Modeling of wheat yield and sensitivity analysis based on energy inputs for three years in Abyek town, Ghazvin, Iran

To get a proper energy consumption pattern and an increase in energy productivity, determining a relationship between energy inputs and outputs is necessary.  In this study, the equivalent energy of inputs and outputs data used in wheat production in Abyek town of Ghazvin province, Iran was collected from farmers over three years.  The energy ratio was obtained as 2.11, 2.08 and 2.03 and energy productivity was obtained as 0.15, 0.14 and 0.14 (kg MJ-1) for 2010, 2009 and 2008, respectively.  It was found that the contributions of indirect and non-renewable energies on wheat yield were more than the impacts of direct and renewable energies.  To determine the effects of energy inputs on wheat yield, the Cobb–Douglas production function was used.  Model 1 was composed of individual energy inputs: labor, machinery, electricity, diesel fuel, water for irrigation, fertilizer, chemicals and seed energies  In Model 2 energy inputs divided to direct and indirect energies and in Model 3 they divided to renewable and non-renewable energies.  The R2 values in all three models were more than 0.98 and showed that the models can estimate well.  The sensitivity analysis results for Model I showed that the major marginal physical productivities (MPPs) were water for irrigation, human labor and water for irrigation in 2010, 2009 and 2008, respectively. In Model II, the major MPP belongs to for renewable energy in the same years.   Keywords: energy consumption pattern, Cobb-Dauglas, marginal physical productivity, renewable, return to scal

Estimation of infiltration rate and deep percolation water using feed-forward neural networks in Gorgan Province

The two common methods used to develop PTFs are multiple-linear regression method and Artificial Neural Network. One of the advantages of neural networks compared to traditional regression PTFs is that they do not require a priori regression model, which relates input and output data and in general is difficult because these models are not known. So at present research, we compare performance of feed-forward back-propagation network to predict soil properties. Soil samples were collected from different horizons profiles located in the Gorgan Province, North of Iran. Measured soil variables included texture, organic carbon, water saturation percentage Bulk density, Infiltration rate and deep percolation. Then, multiple linear regression and neural network model were employed to develop a pedotransfer function for predicting soil parameters using easily measurable characteristics of clay, silt, SP, Bd and organic carbon. The performance of the multiple linear regression and neural network model was evaluated using a test data set by R2, RMSE and RSE. Results showed that artificial neural network with two and five neurons in hidden layer had better performance in predicting soil hydraulic properties than multivariate regression. In conclusion, the result of this study showed that both ANN and regression predicted soil properties with relatively high accuracy that showed that strong relationship between input and output data and also high accuracy in determining of data

2010, Continuous mapping of topsoil calcium carbonate using geostatistical techniques in a semi-arid region

Abstract Prediction and mapping of soil calcium carbonate are necessary for sustainable management of soil fertility. So, this research was done with the aims of (1) evaluation and analyzing spatial variability of topsoil calcium carbonate as an aspect of soil fertility and plant nutrition, (2) comparing geostatistical methods such as kriging and co-kriging and (3) continuous mapping of topsoil calcium carbonate. Sampling was done with stratified random method and 23 soil samples from 0 to 15 cm depth were collected. In co-kriging method, salinity data was also used as auxiliary variable. For comparing and evaluation of geostatistical methods, cross validation and statistical parameters such as correlation coefficient and RMSE were considered. The results showed that co-kriging method has the higher correlation coefficient (0.76) and less RMSE (4.1) which means its higher accuracy than kriging method to predict calcium carbonate content

Soil suitability evaluation for crop selection using fuzzy sets methodology

In this study appraisal of four different agricultural land evaluation methods including the so-called Storie method, square root method, maximum limitation method and fuzzy sets method, was done. The study was performed in Bastam region, located in Semnan province at the north east of Iran.<strong> </strong>Three crops including tomato, wheat and potato were assessed for the purpose of this research. Soil characteristics assessed were rooting depth, CaCo_3,organic carboncontent, clay content, pH and slope gradient. Statistical analyses were done at significance levels of <em>α </em>= 0.1 and <em>α</em> = 0.05. Results of regression between land indices, calculated through the four methods, with observed yields of the crops, showed that the regression were significant in fuzzy sets method for all of the assessed crops at <em>p </em>= 0.05 but not significant in maximum limitation method for any of the crops. The Storie and square root methods also showed a significant correlation with wheat yield at <em>p </em>= 0.1. This study was a demonstrative test of fuzzy sets theory in land suitability evaluation for agricultural uses, which revealed that this methodology is the most correct method in given circumstances

Three-dimensional mapping of soil organic carbon using soil and environmental covariates in an arid and semi-arid region of Iran

This study focus on modeling and mapping soil organic carbon (SOC) at high spatial resolution and at four standard depths in an arid and semi-arid region of Iran. The SOC data includes 850 soil samples collected from 278 observation profiles. In parallel, a wide range of environmental covariates ( ) were obtained from multiple sources. Six individual machine learning (ML) algorithms were compared to modeling and predicting SOC. Two scenarios were investigated. The first one accounts for soil and environmental covariates (S1) while the second one only accounts for environmental covariates (S2). Our results show that accounting for soil variables in the prediction (S1) leads to a twofold increase of for all ML algorithms, while random forest (RF) outperformed the other ML approaches at all depths. Whenever possible, using additionally the soil variables that are at hand in a study area is thus beneficial for improving SOC predictions

A neural network model for estimating soil phosphorus using terrain analysis

Artificial neural network (ANN) model was developed and tested for estimating soil phosphorus (P) in Kouhin watershed area (1000 ha), Qazvin province, Iran using terrain analysis. Based on the soil distribution correlation, vegetation growth pattern across the topographically heterogeneous landscape, the topographic and vegetation attributes were used in addition to pedologic information for the development of ANN model in area for estimating of soil phosphorus. Totally, 85 samples were collected and tested for phosphorus contents and corresponding attributes were estimated by the digital elevation model (DEM). In order to develop the pedo-transfer functions, data linearity was checked, correlated and 80% was used for modeling and ANN was tested using 20% of collected data. Results indicate that 68% of the variation in soil phosphorus could be explained by elevation and Band 1 data and significant correlation was observed between input variables and phosphorus contents. There was a significant correlation between soil P and terrain attributes which can be used to derive the pedo-transfer function for soil P estimation to manage nutrient deficiency. Results showed that P values can be calculated more accurately with the ANN-based pedo-transfer function with the input topographic variables along with the Band 1

GIS based evaluation of land suitability: A case study for major crops in Zanjan University region

The knowledge of land resources is an essential need, especially in developing countries, where resources are often scarce. To reduce the human influence on natural resources and to identify an appropriate land use, it is essential to carry out scientific land evaluations. A land suitability assessment was carried out in Zanjan University region for three major crops and apple orchards. In this area crops such as wheat, barley and maize take a special role in economical status of the area. For this study, a historical and a recent remote sensing-derived map was homogenized to increase accuracy. Also, GIS has been used to match the suitability for main crops based on the requirements of the crops and the quality and characteristics of land. Different land quality parameters, viz. soil texture, depth, erosion, slope, flooding and coarse fragments under various land units were evaluated for the crops. Only about 27% of the area was found to be highly suitable for wheat. The percentage of first class suitability of wheat was much higher than that of barley (13.9%). More than 90% of the total area was classified moderately suitable for maize. The present orchard area is not in accordance with the land qualities. Land units with severe limitations are due to organic matter (OM), gravel and carbonates. Implementation of this procedure should help achieve suitable use of land resources and prevent land degradation in the area. Hence, there is an urgent need for a good land suitability assessment so that the appropriate crops can be grown on these marginal areas of steep highlands. It was also found that with the help of GIS, it is easy to develop a framework for the optimum use of land area