滑推式开沟器设计与作业性能优化试验

针对播种深施肥时,播种开沟器回土能力差,导致播种深度难以控制,结合滑推工作原理,设计出一种新型滑推式开沟器。该开沟器具有上宽下窄的结构,易将扰动土推回沟内,具有良好的回土性能,进而容易控制种子的深度。为探究滑推式开沟器的工作性能,在下宽度和滑推高度固定时,以前进阻力、回土量作为试验指标,以滑推角、开沟器上宽度为试验因素,在室内土槽中进行了单因素试验和中心复合表面组合试验(central composite face-centered design,CCF),对试验数据进行分析检验,得到可信的回归数学模型,分析因素及其交互作用对滑推式开沟器工作性能的影响规律,利用遗传算法NSGA-Ⅱ对回归数学模型进行多目标优化,获得最优的结构参数组合,并进行试验验证。试验结果表明,滑推角对回土量和前进阻力的影响程度均大于上宽度;滑推式开沟器最优的结构参数组合:下宽度为30 mm、上下宽度高度差为150 mm、上宽度为54 mm和滑推角为48°,该条件下的回土量为8.67 g/cm,前进阻力为925.56 N;对最优结构参数组合进行试验验证,得到平均回土量为7.6 g/cm、平均工作阻力为935.78 N,与优化结果比,其相对误差分别为14.8%、1.1%,表明优化结果可信。该研究为开沟器的设计和应用提供新的思路和借鉴。

施水播种条件下灌溉水入渗模型研究

运用土壤动力学原理建立了灌溉水入渗的数学模型 ,并进行了实际灌水的试验验证 ,试验结果表明所建模型的模拟结果与试验结果吻合较好。应用模型模拟分析发现 :适量增加沟宽或回土量可以减小入渗时间 ,缩短播种机排水口至排种口距离。

马铃薯播种机分体式滑刀开沟器参数优化与试验

针对马铃薯播种机的铧靴式和圆盘式开沟器存在的种薯株距分布不均、播种效果质量差、回土量小等问题,研制出分体式滑刀马铃薯开沟器,并进行关键参数优化,分析开沟和回土过程中土壤颗粒在开沟器上的运动规律,研究种薯落入垄沟后的运动过程,确定影响土壤覆盖种薯效果的因素。采用旋转正交的试验设计方法,以机具前进速度、开沟器侧面夹角、开沟器长度及开沟深度为试验因素,以种薯的横向偏移系数、株距变异系数和回土量为试验指标,分析实施田间试验结果,优化分体式滑刀开沟器的结构参数与最优运动参数:在前进速度为0.95 m/s、开沟器侧面夹角为60°、开沟器长度为500 mm、开沟深度为115 mm时,株距变异系数为10.8%、种薯的横向偏移系数为3.83 mm、回土量为24.1%,满足马铃薯播种作业的要求。该研究结果可为马铃薯开沟播种装置设计与应用提供参考。

Quality evaluation of layerlike backfilling and flow pattern of backfill slurry in stope

Stability condition and quality evaluation formula of layerlike backfilling roof,Q≥C,where Q denotes is quality index depending on allowable compressive or tensile strength and integrity of backfilling,and C is the technical index depending on mining method and backfilling technology,were inferred according to simply supported beam theorem.Technical treatment measures for instable backfilling roof,including optimum of appropriate filling materials and dosage for excellent flow property and reduction of backfill cost.It is proved that slope equation of backfill slurry in a stope to be filled is y=hexp[x2/(2σ)2)],where h is height of cone and σ2 is mean square,and that optimum drainage point of backfill slurry can be determined by the equation and sizes of stope.Case study indicates that the results can give a theoretical support for quality evaluation and control of layerlike backfilling.

Prediction of resilient modulus for subgrade soils based on ANN approach

The resilient modulus(MR)of subgrade soils is usually used to characterize the stiffness of subgrade and is a crucial parameter in pavement design.In order to determine the resilient modulus of compacted subgrade soils quickly and accurately,an optimized artificial neural network(ANN)approach based on the multi-population genetic algorithm(MPGA)was proposed in this study.The MPGA overcomes the problems of the traditional ANN such as low efficiency,local optimum and over-fitting.The developed optimized ANN method consists of ten input variables,twenty-one hidden neurons,and one output variable.The physical properties(liquid limit,plastic limit,plasticity index,0.075 mm passing percentage,maximum dry density,optimum moisture content),state variables(degree of compaction,moisture content)and stress variables(confining pressure,deviatoric stress)of subgrade soils were selected as input variables.The MR was directly used as the output variable.Then,adopting a large amount of experimental data from existing literature,the developed optimized ANN method was compared with the existing representative estimation methods.The results show that the developed optimized ANN method has the advantages of fast speed,strong generalization ability and good accuracy in MR estimation.

Estimation of As and Cu Contamination in Agricultural Soils Around a Mining Area by Reflectance Spectroscopy:A Case Study

Concentrations of Iron (Fe), As, and Cu in soil samples from the fields near the Baoshan Mine in Hunan Province, China, were analyzed and soil spectral reflectance was measured with an ASD FieldSpec FR spectroradiometer (Analytical Spectral Devices, Inc., USA) under laboratory condition. Partial least square regression (PLSR) models were constructed for predicting soil metal concentrations. The data pre-processing methods, first and second derivatives (FD and SD), baseline correction (BC), standard normal variate (SNV), multiplicative scatter correction (MSC), and continuum removal (CR), were used for the spectral reflectance data pretreatments. Then, the prediction results were evaluated by relative root mean square error (RRMSE) and coefficients of determination (R 2 ). According to the criteria of minimal RRMSE and maximal R 2 , the PLSR models with the FD pretreatment (RRMSE = 0.24, R 2 = 0.61), SNV pretreatment (RRMSE = 0.08, R 2 = 0.78), and BC-pretreatment (RRMSE = 0.20, R 2 = 0.41) were considered as the final models for predicting As, Fe, and Cu, respectively. Wavebands at around 460, 1 400, 1 900, and 2 200 nm were selected as important spectral variables to construct final models. In conclusion, concentrations of heavy metals in contaminated soils could be indirectly assessed by soil spectra according to the correlation between the spectrally featureless components and Fe; therefore, spectral reflectance would be an alternative tool for monitoring soil heavy metals contamination.

Nitrogen Recoveries and Yields Improvement in Cowpea sorghum and Fallow sorghum Rotations in West Africa Savannah

The effects of previous cowpea （Vignaunguiculata） and annual fallow on N recoveries, succeeding sorghum yields and soil properties were studied using a 5-year-old （1995-1999） field experiment at Kouar6 （11°59′ North, 0°19′ West and 850 m altitude） in Burkina Faso. A 3 4 factorial design in a split plot arrangement with three rotation treatments and four fertilizer treatments was used. Total N uptake by succeeding sorghum increased from 26 kg N ha~ in mono cropping of sorghum to 31 and 48 kg N ha~ when sorghum was rotated with fallow or cowpea respectively. Nitrogen derived from fertilizer increased from 10% in mono cropping of sorghum to 22% and 26% when sorghum was rotated with fallow or cowpea respectively. While fallow did not increase N derived from soil, cowpea doubled the quantity of N derived from soil （Ndfs）. Sorghum grain yields increased from 75% and 100% when sorghum was rotated with fallow or cowpea respectively. All rotations treatments decreased soil organic C and N but soil organic C was the highest in fallow-sorghum rotation. It was concluded that cowpea-sorghum rotation was more effective than fallow-sorghum rotation and five management options were suggested to improve traditional system productivity.

Neuro-fuzzy systems in determining light weight concrete strength

The adaptive neuro-fuzzy inference systems(ANFIS)are widely used in the concrete technology.In this research,the compressive strength of light weight concrete was determined.To this end,the scoria percentage and curing day variables were used as the input parameters,and compressive strength and tensile strength were used as the output parameters.In addition,100 patterns were used,70%of which were used for training and 30%were used for testing.To assess the precision of the neuro-fuzzy system,it was compared using two linear regression models.The comparisons were carried out in the training and testing phases.Research results revealed that the neuro-fuzzy systems model offers more potential,flexibility,and precision than the statistical models.

Allometric prediction of above-ground biomass of eleven woody tree species in the Sudanian savanna-woodland of West Africa

Allometric models are necessary for estimating biomass in terrestrial ecosystems. Generalized allometric relationship exists for many tropical trees, but species- and region-specific models are often lacking. We developed species-specific allometric models to predict aboveground biomass for 11 native tree species of the Sudanian savanna- woodlands. Diameters at the base and at breast height, with species means ranging respectively from 11 to 28 cm and 9 to 19 cm, and the height of the trees were used as predictor variables. Sampled trees spanned a wide range of sizes including the largest sizes these species can reach. As a response variable, the biomass of the trees was obtained through destructive sampling of 4 754 trees during wood harvesting. We used a stepwise multiple regression analysis with backward elimination procedure to develop models separately predicting, total biomass of the trees, stem biomass, and biomass of branches and twigs. All species- specific regression models relating biomass with measured tree dimen- sions were highly significant （p 〈 0.001）. The biomass of branches and twigs was less predictable compared to stem biomass and total biomass, although their models required fewer predictors and predictor interac- tions. The best-fit equations for total above-ground biomass and stem biomass bad R2 〉 0.70, except for the Acacia species; for branches including twig biomass, R2-values varied from 0.749 for Anogeissus leiocarpa to 0.183 for Acacia macrostachya. The use of these equations in estimating available biomass will avoid destructive sampling, and aid in planning for sustainable use of these species.

Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations

Soil visible-near infrared diffuse reflectance spectroscopy(vis-NIR DRS)has become an important area of research in the fields of remote and proximal soil sensing.The technique is considered to be particularly useful for acquiring data for soil digital mapping,precision agriculture and soil survey.In this study,1581 soil samples were collected from 14 provinces in China,including Tibet,Xinjiang,Heilongjiang,and Hainan.The samples represent 16 soil groups of the Genetic Soil Classification of China.After air-drying and sieving,the diffuse reflectance spectra of the samples were measured under laboratory conditions in the range between 350 and 2500 nm using a portable vis-NIR spectrometer.All the soil spectra were smoothed using the Savitzky-Golay method with first derivatives before performing multivariate data analyses.The spectra were compressed using principal components analysis and the fuzzy k-means method was used to calculate the optimal soil spectral classification.The scores of the principal component analyses were classified into five clusters that describe the mineral and organic composition of the soils.The results on the classification of the spectra are comparable to the results of other similar research.Spectroscopic predictions of soil organic matter concentrations used a combination of the soil spectral classification with multivariate calibration using partial least squares regression(PLSR).This combination significantly improved the predictions of soil organic matter(R2=0.899;RPD=3.158)compared with using PLSR alone(R2=0.697;RPD=1.817).