Optimization of Land Use Based on Land Suitability Class for the Development of Red Chillies in Jember Regency

Chilli (Capsicum annum L.) is a horticulture comodities that high demand because it has a high economic value. The demand of Chilli increase every year as the population grows and the industry grows which requires Chilli as raw materials. The productivity of red chili in Jember Regency is also low, which is only 87.68 quintals hectare-1. Fluctuations in red chili production that occurred in the Jember area were caused by several things including an unsupportive environment such as declining soil fertility, high pest attacks, and excessive use of pesticides. Therefore there is a need for agricultural development in order to be able to meet food needs and increase export opportunities to neighboring countries. The parameters observed in this study were secondary data from the soil unit map. The secondary data referred to are soil drainage, soil depth in cm, slope related to erosion, altitude, standing water, soil cation exchange capacity, and soil pH. The improvement efforts made to improve the land suitability class is by setting the spacing, giving organic matter, agricultural lime, P fertilization, cutting and filling the land with a mound terrace system or bench terrace to reduce soil erosion.

Effects of Fertilisation Using Organic Waste Products with Mineral Complementation on Sugarcane Yields and Soil Properties in a 4 Year Field Experiment

Sugarcane cultivation is suitable for the exploitation of organic waste products. However, minimum complementary mineral input is necessary for optimal fertilisation. Control mineral fertilisation treatments with mulch (MCM) or without mulch (MC) were compared with two organic waste treatments, a pig slurry with mulch (PSM) and without mulch (PS), and a sugarcane vinasse with mulch (SVM) and without mulch (SV) on a Nitisol in French Reunion Island. The sugarcane yields obtained with the different treatments differed each year. However, no trend was observed and no significant and recurrent effect of the presence of mulch or of the different treatments was identified over the course of the 4 year experiment. Soil pHw and pH KCl measured in the different treatments increased from year 3 in with the treatments including organic waste products (PS, PSM, SV and SVM) but remained constant with the treatments including only mineral fertilisation (MC and MCM). With the exception of PS and PSM, which were significantly higher in year 4, soil organic carbon content was not modified by the treatments. Soil cation exchange capacity increased only slightly with the PS and PSM treatments from year 3 on. The differences in yields and soil properties can be explained by the nature of the organic waste products, the accumulation of nutrients after several applications, and the specific characteristics of the sugarcane crop. The improvement in soil properties from the third year on was not reflected in the yield of sugarcane because it was too weak, and the crop explores a much larger volume of soil.

New hybrid nature-based algorithm to integration support vector machine for prediction of soil cation exchange capacity

Soil cation exchange capacity (CEC) strongly influences the chemical, physical, and biological properties of soil. As the direct measurement of the CEC is difficult, costly, and time-consuming, the indirect estimation of CEC from chemical and physical parameters has been considered as an alternative method by researchers. Accordingly, in this study, a new hybrid model using a support vector machine (SVM), coupling with particle swarm optimization (PSO), and integrated invasive weed optimization (IWO) algorithm is developed for estimating the soil CEC. The physical and chemical data (i.e., clay, organic matter (OM), and pH) from two field sites of Taybad and Semnan in Iran were used for validating the new proposed approach. The ability of the proposed model (SVM-PSOIWO) was compared with the individual model (SVM) and the hybrid model (SVM-PSO). The results of the SVM-PSOIWO model were also compared with those of existing studies. Different performance evaluation criteria such as RMSE, R2, MAE, RRMSE, and MAPE, Box plots, and scatter diagrams were used to test the ability of the proposed models for estimation of the CEC values. The results showed that the SVM-PSOIWO model with the RMSE (R2) of 0.229 Cmol + kg−1 (0.924) was better than those of the SVM and SVM-PSO models with the RMSE (R2) of 0.335 Cmol + kg−1 (0.843) and 0.279 Cmol + kg−1 (0.888), respectively. Furthermore, the ability of the SVM-PSOIWO model compared with existing studies, which used the genetic expression programming, artificial neural network, and multivariate adaptive regression splines models. The results indicated that the SVM-PSOIWO model estimates the CEC more accurately than existing studies.

Fine Root Traits of Pinus koraiensis Varied with Soil Cation Exchange Capacity in Natural Forests

Adaptation of fine root plasticity to soil nutrients heterogeneity in natural forest ecosystems has not been well explored. The study aimed to determine seasonal variations of fine root traits in Pinus koraiensis natural forests and explore the relationship between fine root traits and soil properties. Root and soil samples were collected from the coniferous broad-leaved mixed forest (BP), monospecific P. koraiensis forest (MP), and coniferous mixed forest (CP). Soil available phosphorus (P) content for MP was 31.7% and 39.8% lower than for BP and CP. Soil cation exchange capacity (CEC) for MP was lower by 23.5% and 27.2% than for BP and CP, respectively. In July, specific root lengths and root surface areas for BP and CP (mixed forests) were significantly higher than for MP (monospecific forest). A structural equation model showed that CEC had a significant effect on fine root traits. Root diameter, root volume density, and root surface area density were negatively correlated with CEC. Fine root traits plasticity of P. koraiensis are closely related to soil available nutrient contents, CEC, and species composition at the ecosystem level.

Effect of CaCO3 Form on Soil Inherent Quality Properties of Calcareous Soils

Four soil samples different in there CaCO3 forms contents (total, active and active /total CaCO3) were taken from tillage horizon 0-30 cm depth to study the impacts of CaCO3 form on some soil inherent quality indicators in calcareous soils. Result revealed that the total CaCO3 forms have a great influence on particle size distribution to make it coarser in general comparing with the absence of CaCO3 with soil particles. The increase of active /total (A/T CaCO3%) till 50% has a positive effect on increasing soil electrical conductivity E.C ds.m-1, while increment of A/T CaCO3 over 50% till 70% detrimentally reduced soil E.C ds.m-1 hundred fold. The elevation of (A/T CaCO3%) has inverse correlation with soil cation exchange capacity (CEC Cmole.kg soil). the elevation of (A/T CaCO3%) has inverse correlation with soil cation exchange capacity (CEC Cmole.kg soil) the (A/T CaCO3%) ratio over 56% till 69% significantly improves the mean weight diameter and macro aggregate. In general the optimum total CaCO3 for micro aggregate formation may not exceed 20%. The best Total and (A/T CaCO3%) for better aggregate stability is between 20 to 48% . The proper total calcium carbonate for better total heterotrophic aerobic bacterial activity is around 20% . The best growth of the Azotobacter species which is belonging to non-symbiotic nitrogen fixing bacteria is obtained when total CaCO3 ranges between 10-20% or between 48-56% of (A/T CaCO3%).

Comparison of Artificial Neural Network and Regression Pedotransfer Function for prediction of soil cation exchange capacity at Iraq, Ray AL Jazeera, Mosul region: مقارنة بين نموذجي الشبكات العصبية والانحدار الخطي المتعدد في تخمين السعة التبادلية الكاتيونية للتربة باستخدام الدوال التحويلية في العراق في منطقة ري الجزيرة في مدينة الموصل

The study of soil characteristics such as the ability to exchange positive ions CEC (Cation Exchange Capacity) play a significant part in study of ecological researches, also it is important for decision concerning pollution prevention and crop management. CEC represents the number of negative charges in soil, since direct method for measuring CEC are cumbersome and time consuming Lead to the grow of indirect technique in guessing of soil CEC property. Pedotransfer function (PTFs) is effective in estimating this parameter of easy and more readily available soil properties, 80 soil sample was taken from diverse horizons of 20 soil profiles placed in the Aljazeera Region, Iraq. The aim of this study was to compare Neural Network model (feed forward back propagation network) and Stepwise multiple linear regression to progress a Pedotransfer function for forecasting soil CEC of Mollisols and Inseptisols in Al Jazeera Irrigation Project using easily available features such as clay, sand and organic matter. The presentation of Neural Network model and Multiple regression was assessed using a validation data set. For appraise the models, Mean Square Error (MSE) and coefficient of determination R2 were used. The MSE and R2 resultant by ANN model for CEC were 2.2 and 0.96 individually while these result for Multiple Regression model were 3.74 and 0.88 individually. Results displayed 8% improvement in increasing R2 and also improvement 41% for decreasing MSE for ANN model, this pointed that artificial neural network with three neurons in hidden layer had improved achievement in forecasting soil cation exchange capacity than multiple regression. So we can conclude that ANN model by use (MLP) multilayer perceptron for predicting CEC from measure available soil properties have more accuracy and effective compared with (MLR) multiple linear regression model.