Simulation of the effect of pruning and topping on cotton growth using COTTON2K model

COTTON2K, like most other cotton production models, does not always adequately represent local growth conditions owing to the fact that it fails to take into account some indigenous cultivation practices. For instance, pruning and topping, a common practice for cotton cultivation in China is not included in the model simulation of COTTON2K. The objective of this research therefore was to: (1) modify COTTON2K source code and slot pruning and topping simulation switch on to the model, and (2) calibrate and validate the modified COTTON2K model with field data from pruning and topping cultivation practice. First, field collected data in 2003 and 2004 were compared between the treatments, with and without pruning and topping, and the COTTON2K source code updated with the ‘pruning and topping’ switch. This was followed by the calibration and validation of the updated model with field data and simulation of the effect of pruning and topping on cotton performance. It was noted from field observations that pruning and topping reduced total fruit sites, but at the same time, increased retained boll number, possibly due to significant reductions in abscised fruit sites. Though total dry matter production dropped, more dry matter allocation to reproductive organs, however, enhanced higher cotton lint yield in the pruning and topping treatment. Results of the modified model simulation showed that growth in the number of main-stem node ceased after topping. Furthermore, there was more biomass allocation to reproductive organs, such as green and open bolls under pruning and topping. Coefficient of determination above 0.8 for most of the growth factors was obtained in the calibration and validation processes under pruning and topping, a strong indication of the level of success of the model modification.     

A flexible regression model for diameter prediction

We present a functional regression model for diameter prediction. Usually stem form is estimated from a regression model using dbh and height of the sample tree as predictor. With our model additional diameter observations measured at arbitrary locations within the sample tree can be incorporated in the estimation in order to calibrate a standard prediction based on dbh and height. For this purpose, the stem form of a sample tree is modelled as a smooth random function. The observed diameters are assumed as independent realizations from a sample of possible trajectories of the stem contour. The population average of the stem form within a given dbh and height class is estimated with the taper curves applied in the national forest inventory in Germany. Tree deviation from the population average is modelled with the help of a Karhunen–Loève expansion for the random part of the trajectory. Eigenfunctions and scores of the Karhunen–Loève expansion are estimated through conditional expectations within the methodological framework of functional principal component analysis (FPCA). In addition to a calibrated estimation of the stem form, FPCA provides asymptotic pointwise or simultaneous confidence intervals for the calibrated diameter predictions. For the application of functional principal component analysis modelling the covariance function of the random process is crucial. The main features of the functional regression model are discussed informally and demonstrated by means of practical examples.     

Soil texture, climate and management effects on plant growth, grain yield and water use by rainfed maize–wheat cropping system: Field and simulation study

In sub-mountain tract of Punjab state of India, maize (Zea mays, L.) and wheat (Triticum aestivum L.) crops are grown as rainfed having low crop and water productivity. To enhance that, proper understanding of the factors (soil type, climate, management practices and their interactions) affecting it is a pre-requisite. The present study aims to assess the effects of tillage, date of sowing, and irrigation practices on the rainfed maize–wheat cropping system involving combined approach of field study and simulation. Field experiments comprising 18 treatments (three dates of sowing as main, three tillage systems as subplot and two irrigation regimes as the sub-subplot) were conducted for two years (2004–2006) and simulations were made for 15 years using CropSyst model. Field and simulated results showed that grain yields of maize and wheat crops were more in early July planted maize and early November planted wheat on silt loam soil. Different statistical parameters (root mean square error, coefficient of residual mass, model efficiency, coefficient of correlation and paired t-test) indicated that CropSyst model did fair job to simulate biomass production and grain yield for maize–wheat cropping system under varying soil texture, date of planting and irrigation regimes.     

计算机视觉摄影测量三维控制场设计

为利用计算机视觉进行摄像机摄影测量而进行了三维控制场的设计,分析了控制标志的尺寸、形状、颜色的设计方法,构造了三维控制场,利用设计的控制场进行摄测模型试验,获得了较高的标定测量精度。     

样本集选择对稻谷千粒重NIR模型预测精度的影响

为研究样本集选择方法对稻谷千粒重NIR模型的影响,分别采用不同数量样品,不同定标集、验证集比例以及不同定标集选择方法,选出建模的定标集,在600~1100 nm的波长区间,用偏最小二乘法建立稻谷千粒重的近红外光谱预测模型,根据内部交叉验证决定系数(Rv2)、外部验证决定系数(Rp2)、内部交叉验证误差(SECV)和预测误差(SEP)比较模型的预测能力。结果显示,样品数量、定标集和验证集比例以及定标集选择方法均对稻谷千粒重的NIR模型有明显影响。采用合适数量的样品可以得到较佳的NIR模型,以7∶3的比例分割定标集与验证集,得到的稻谷千粒重NIR模型具有相对高的预测能力,而与含量梯度法和随机抽取法相比,采用K-S算法进行定标集选择,可以得到预测精度更高的NIR模型。     

Field experiments and simulation to evaluate rice cultivar adaptation to elevated carbon dioxide and temperature in sub-tropical India

Location specific adaptation option is required to minimize adverse impact of climate change on rice production. In the present investigation, we calibrated genotype coefficients of four cultivars in the CERES-Rice model for simulation of rice yield under elevated CO₂ environment and evaluation of the cultivar adaptation in subtropical India. The four cultivars (IR 36, Swarna, Swarn sub1, and Badshabhog) were grown in open field and in Open Top Chamber (OTC) of ambient CO₂ (≈390 ppm) and elevated CO₂ environment (25% higher than the ambient) during wet season (June–November) of the years 2011 and 2012 at Kharagpur, India. The genotype coefficients; P1 (basic vegetative phase), P2R (photoperiod sensitivity) and P5 (grain filling phase) were higher, but G1 (potential spikelet number) was lower under the elevated CO₂ environment as compared to their open field value in all the four cultivars. Use of the calibrated model of elevated CO₂ environment simulated the changes in grain yield of −13%, −17%, −4%, and +7% for the cultivars IR 36, Swarna, Swarna sub1, and Badshabhog, respectively, with increasing CO₂ level of 100 ppm and rising temperature of 1 °C as compared to the ambient CO₂ level and temperature and they were comparable with observed yield changes from the OTC experiment. Potential impacts of climate change were simulated for climate change scenarios developed from HadCM3 global climate model under the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios (A2 and B2) for the years 2020, 2050, and 2080. Use of the future climate data simulated a continuous decline in rice grain yield from present years to the years 2020, 2050 and 2080 for the cultivars IR 36 and Swarna in A2 as well as B2 scenario with rising temperature of ≥0.8 °C. Whereas, the cultivar Swarna sub1 was least affected and Badshabhog was favoured under elevated CO₂ with rising temperature up to 2 °C in the sub-tropical climate of India.     

基于RBF神经网络的土壤含水量传感器标定方法(英文)

土壤含水量时空变异对作物生长、农田气候变化等领域的研究具有重大意义。为了克服TDR-3土壤水分传感器输出电压的非线性缺点,提高土壤含水量数据采集以及计算效率,该研究将TDR-3土壤水分传感器与无线传感器网络结合,提出了土壤水分含量的RBF神经网络标定方法。以20个带标号的水杯作为承载砖红壤土和水的载体,对其中的样本进行数据采集,经多次测量取平均值。为验证标定算法的准确性,同时列举出以土净重、加水质量、水分含量TDR-3测量值为属性,以测试水土比值为目标训练样本,以RBF神经网络为标定算法的拟合结果。为了更直观地展示试验结果,以散点图方式分别展示水分含量TDR-3测量值与实际水土比的TDR-3土壤水分含量曲线;水分含量TDR-3测量值与RBF神经网络拟合水土比的RBF神经网络拟合土壤水分含量曲线;以及实际水土比与RBF神经网络拟合水土比的散点图。为验证RBFNN拟合水土比值的相关性,引入皮尔逊相关系数。5次试验,得到5组皮尔逊相关系数,分别为0.9745,0.9832,0.9798,0.9804及0.9789,都接近于1,说明真实测试数据与拟合数据相关性很强,且为正相关关系。可见,该法能取得较好的标定效果,并且简单、实用、可行,为土壤含水量的实时监测提供了一种有效的方法。     

近红外漫反射光谱法测定青贮玉米品质性状的研究

【目的】研究利用近红外漫反射光谱法（NIDRS）测定青贮玉米的体外干物质消化率 (IVDMD)、中性洗涤纤维(NDF)、酸性洗涤纤维 (ADF)、粗蛋白（CP）和粗脂肪（EE）含量的可行性。【方法】以普通、高油和超高油玉米全株和秸秆的青贮样为材料，采用光谱的主成分空间技术和偏最小二乘回归法（PLS）。【结果】所建立的IVDMD、NDF、ADF、CP和EE的校正模型的交叉验证决定系数(R2cv)分别为0.9133、0.9764、0.9789、0.9254和0.7294，外部验证决定系数(R2val)分别为0.8879、0.9455、0.9635、0.9387和0.7333，各项误差(RMSEE、RMSECV和RMSEP)为0.24(CP)～2.23(NDF)。【结论】利用近红外漫反射光谱法测定青贮玉米品质性状是完全可行的，该结果可满足畜牧业对青贮饲料品质快速分析的需要，对青贮玉米育种材料的快速鉴定筛选具有重要的意义。     

A comparison of WEPP and SWAT for modeling soil erosion of the Zhangjiachong Watershed in the Three Gorges Reservoir Area

Soil and water conservation is important for the Three Gorges Reservoir Area in China, and quantification of soil loss is a significant issue. In this study, two widely used models - the Water Erosion Prediction Project (WEPP) and the Soil and Water Assessment Tool (SWAT) - were applied to simulate runoff and sediment yield for the Zhangjiachong Watershed in the Three Gorges Reservoir Area. The models were run and the simulated runoff and sediment yield values were compared with the measured runoff and sediment yield values. In the calibration period, the model efficiency (E_NS) values for the WEPP and SWAT were 0.864 and 0.711 for runoff, and 0.847 and 0.678 for sediment yield, respectively. In the validation period, the E_NS values for WEPP and SWAT were 0.835 and 0.690 for runoff, and 0.828 and 0.818 for sediment yield, respectively. The results of E_NS and the other criteria indicate that the results of both models were acceptable. WEPP simulations were better than SWAT in most cases, and could be used with a reasonable confidence for soil loss quantification in the Zhangjiachong Watershed.     

基于物联网的日光温室环境监测在线校准

温室环境条件特别是温度对于作物生长和发育具有十分显著的影响。日光温室调控的主要环境因子之一是温度。然而,自然环境下的光照对温度产生作用,影响空气温度的监测精度。采用机器学习中的支持向量机算法(SVM),对日光温室内的温度智能监测算法进行了研究,根据光照情况对实时监测的温度数据进行校准。通过与实验测量的数据进行对比分析,结果表明：所提出的监测方法可以较为准确地实时监测空气温度,从而无需使用隔热材料或者遮阳处理,就可以基于监测的数据更精确地对相应的环境因素进行调节。基于该方法,可采用常用的工业设备实现温室大棚内实时温度数据的监测,既可以节约设备和人力成本,又可以为温室控制提供准确的数据。