Soil Fertility Comparison between Rotations of Tobacco-Rice (TR) and Rape-Rice (RR)

Tobacco-planting plays an important role in ensuring the high-quality tobacco raw materials supply and the local social and economic development in Chenzhou City. In recent years whether tobacco-planting is better for the maintenance and improvement of soil fertility than other crop-planting has been highly concerned. In this study, 16 soil fertility indicators and soil integrated index (IFI) were compared by 21 pairs of fields in Chenzhou city under the rotations of tobacco-rice (TF) and rice-rice (RR), and results showed that, comparing the mean values of soil fertility indicators, the contents of OM, TN, AN, AK, S and IFI were extremely significantly higher in TR than those in RR (p < 0.01), the contents of Cu, Ca, Mg and Fe were significantly higher in TR than those in RR (p < 0.05), but Mn content was significantly lower in TR than those in RR (p < 0.05). Meanwhile the contents of TP, TK and AP were insignificantly higher in TR than those in RR, and the contents of B, Mo and Zn were insignificantly lower in TR than those in RR. The above significant differences in soil fertility indicators were mainly due to relatively higher fertilizer inputs and less nutrient removal during tobacco-growing season than during rape-growing season, the net increase of N, P2O5 and K2O are 8.61, 5.25 and 24.89 kg per 667 m2 respectively in tobacco-growing season, while the net decrease of N, P2O5 and K2O are 8.88, 4.70 and 4.62 kg per 667 m2 respectively in rape-growing season. C.V. of soil fertility indicators and IFI were meanly lower in TR (52.25% and 15.95%, respectively) than those in RR (63.07% and 22.12%, respectively). Comparatively, tobacco-planting can improve soil fertility better than rape-planting when rotated with late rice in Chenzhou city. For tobacco-planting, Mg fertilizer should be applied for 23.8% TR fields, while more N, K, Ca, Mg, S and B fertilizers should be applied for 42.86%, 23.81%, 14.29%, 47.62%, 80.95% and 47.62% RR fields, respectively.

Diagnosis of Soil Nutrient Constraints in Small-Scale Groundnut （Arachis hyopaea L,） Production Systems of Western Kenya Using Infrared Spectroscopy

Wise decision-making on resource allocation and intervention targeting for soil management cannot rely solely on trial and error methods and field observations used by small-scale farmers： cost-effective soil fertility survey methods are needed. This study aimed to test the applicability of infrared spectroscopy （IR） as a diagnostic screening tool for making soil fertility recommendations in small-scale production systems. Soil fertility survey of 150 small-scale groundnut farms in western Kenya was conducted using a spatially stratified random sampling strategy. Soil properties examined were pH in water （pHw）, total carbon （C）, total nitrogen （N）, extractable phosphorus （P）, exchangeable potassium （K）, calcium （Ca）, magnesium （Mg） and texture. These properties were calibrated to mid-infrared （MIR） diffuse reflectance using partial least square regression （PLSR）. Cross-validated coefficient of determination （r2） values obtained from calibration models were 〉 0.80 for all properties, except P and K with 0.66 and 0.50 respectively. Soil nutritional deficiencies were evaluated using critical nutrient limits based on IR predictions and composite soil fertility indices （SFIs） developed from the soil properties using principal component analysis. The SFIs were calibrated to MIR soil spectral reflectance with cross-validated r： values 〉 0.80. The survey showed that 56% of the groundnut farms had severe soil nutrient constraints for production, especially exchangeable Ca, available P and organic matter. IR can provide a robust tool for farm soil fertility assessment and recommendation systems when backed up by conventional reference analyses. However, further work is required to test direct calibration of crop responses to spectral indicators and to improve prediction of extractable P and K tests.

Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters

The soil,Soul of Infinite Life,is the entity responsible for sustaining life on earth.In spite of significant advances in the service sector,agriculture remains the major provider of employment and source of revenue in India.Soil testing is a valuable tool for evaluating the available nutrient status of soil and helps to determine the proper amount of nutrients to be added to a given soil based on its fertility and crop needs.In the current study,the soil test report values are used to classify several significant soil features like village wise soil fertility indices of Available Phosphorus(P),Available Potassium(K),Organic Carbon(OC)and Boron(B),as well as the parameter Soil Reaction(pH).The classification and prediction of the village wise soil parameters aids in reducing wasteful expenditure on fertilizer inputs,increase profitability,save the time of chemical soil analysis experts,improves soil health and environmental quality.These five classification problems are solved using the fast learning classification technique known as Extreme Learning Machine(ELM)with different activation functions like gaussian radial basis,sine-squared,hyperbolic tangent,triangular basis,and hard limit.After the performance analysis of ELMs with diverse activation functions for these soil parameter classifications,the gaussian radial basis function attains the maximum performance for four out of five problems,which goes above 80%in most of the accuracy rate calculations in every problem,followed by hyperbolic tangent,hard limit,triangular basis,and sine-squared.However,the performance of the final classification problem,i.e.the pH classification,gives moderate values with the gaussian radial basis and best performance(near 90%),with the hyperbolic tangent.