Estimating the mass of mango fruit (Mangifera indica, cv. Chok Anan) from its geometric dimensions by optical measurement

Uniform fruit mass and shapes of mangoes for export are required in order to ease handling and transportation, and to help satisfy consumer preferences. However, with its irregular shape, one which cannot be approximated by using standard geometrical figures, the mango is not as easy to model as round or oval-shaped fruits.In this study, more than 1000 mango ‘Chok Anan’ fruit were measured with respect to their length, maximum width and maximum thickness, to within an accuracy of 1.0  mm, and their weight was determined to within an accuracy of 0.1  g. Pictures were taken of 30 fruit, through the use of digital cameras, manually rectified and analyzed by use of “Measure 2.1” a computer code.An equation was proposed in order to calculate the mass of the mangoes based on three geometric dimensions, which then showed a high level of explanatory power of R² = 0.97. The root mean standard error of 12.22 was determined, and the mean relative deviation found was 0.05. Based on measurements taken from the digital photos, the equation was then used to determine the estimated mass based on three dimensions. Estimated and measured values were plotted against each other. A high correlation (R² = 0.96) was found between measured and calculated mass.It was shown that the mass can be estimated based on data obtained from two orthogonally taken photographs.     

Estimating the variability of tillage forces on a chisel plough shank by modeling the variability of tillage system parameters

In this paper, a probabilistic approach is proposed for quantifying the variability of the tillage forces for the shank of a chisel plough with narrow tines and to estimate the failure probability. An existing three-dimensional analytical model of tool forces from McKyes was used to model the interaction between the tillage tools and the soil. The variability of tillage forces was modeled, taking into account the variability of soil engineering properties, tool design parameters and operational conditions. The variability of the soil engineering properties was modeled by means of experimental observations. The dispersion effect of each tillage system parameter on the tillage forces was determined by a sensitivity analysis. The results show that the variability of the horizontal and vertical forces follows a lognormal distribution (μ = 0.872, ξ = 0.449; μ = 0.004, ξ = 0.447) and the relationship between these forces is positive and quasi-linear (ρ(P_H, P_v) = 0.93).This lognormal variability was integrated into the estimation of the failure probability for the shank by using Monte Carlo simulation (MCS) and the first-order reliability method (FORM). The results obtained by these two methods, with the assumption of non-correlation between the horizontal and vertical forces, were almost identical. However, the FORM method was faster and simpler, compared to the MCS technique. Furthermore, the correlation between the horizontal and vertical forces has no significant effect on the failure probability, regardless of the correlation strength. Therefore, it is concluded that the FORM method can be used to estimate the failure probability without considering the correlation between horizontal and vertical forces.     

Assessment of forage mass from grassland swards by height measurement using an ultrasonic sensor

The non-destructive assessment of forage mass in legume-grass mixtures as a tool for yield mapping in precision farming applications has been investigated in two field experiments. An ultrasonic sensor was used to determine sward heights. Forage mass-height relationships were evaluated by carrying out static measurements on binary legume-grass mixtures of white clover (Trifolium repens L.), red clover (Trifolium pratense L.), and lucerne (Medicago sativa L.) with perennial rye grass (Lolium perenne L.) across a wide range of sward heights (5.0-104.2 cm) and forage mass (0.15-11.25 t ha⁻¹). Mobile measurements, hereafter referred to as “on-the-go” were conducted by mounting the ultrasonic sensor in combination with a high-precision Differential Global Positioning System (DGPS) on a vehicle. Data were recorded along experimental plots consisting of perennial rye grass and grass-clover mixtures similar to the mixtures that were used for the static experiment. The static experiment revealed a relationship between ultrasonic sward height and forage mass explaining 74.8% of the variance with a standard error (SE) of 1.05 t ha⁻¹ in a common dataset. The type of legume species, weed proportion, and growth period had a significant impact on the above mentioned relationship. Legume-specific regression functions had higher R²-values of up to 0.855 (white clover mixture). Datasets including legume-specific mixtures and pure swards of both components reached comparable R² values between 0.799 and 0.818 but exhibited higher SE values. The abundance of weeds resulted in increased ultrasonic sward heights for the same levels of forage mass. On-the-go measurements across experimental field plots yielded a sward height range of 1.4-70.4 cm. Abrupt forage mass changes at the transition from treatment plots to cut interspaces resulted in a significant deviation from stubble height within a distance of 50 cm to plot borders. When legume-specific equations derived from static measurements were applied to sward heights, forage mass was overestimated by 21.4% on average. Mean residuals from predicted forage mass ranged between 0.893 (pure grass) and 1.672 (red clover mixture) and increased significantly if the point sampling distance along the track was increased to more than 0.82 m on average across all plots. The prediction accuracy of forage mass from ultrasonic height measurements is promising; however, further modifications to the technique are necessary. One such improvement can be the use of spectral reflectance signatures in combination with the ultrasonic sensor.     

Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae)

Detection of crop stress is one of the major applications of hyperspectral remote sensing in agriculture. Many studies have demonstrated the capability of remote sensing techniques for detection of nutrient stress on cotton with only few on pest damage but none so far on leafhopper (LH) severity. Subsequent to introduction of Bt cotton, leafhopper is emerging as a key pest in several countries. In view of its wide host range, geographical distribution and damage potential, a study was initiated to characterise leafhopper stress on cotton, identify sensitive bands, and derive hyperspectral vegetation indices specific to this pest. Cotton plants with varying levels of LH severity were selected from three locations across major cotton growing regions of India. About 57-58 cotton plants from each location exhibiting different levels of LH damage symptoms were selected. Reflectance measurements in the spectral range of 350-2500 nm were made using hyperspectral radiometer. Simultaneously chlorophyll (Chl) and relative water content (RWC) were also estimated from the selected plants. Reflectance from healthy and leafhopper infested plants showed a significant difference in VIS and NIR regions. Decrease in Chl a pigment was more significant than Chl b in the infested plants and the ratio of Chl a/b showed a decreasing trend with increase in LH severity. Regression analysis revealed a significant linear relation between LH severity and Chl (R² = 0.505^∗∗), and a similar fit was also observed for RWC (R² = 0.402^∗∗). Plotting linear intensity curves between reflectance at each waveband with infestation grade resulted in six sensitive bands that exhibited maximum correlation at different regions of the electromagnetic spectrum (376, 496, 691, 761, 1124 and 1457 nm). Regression analysis of several ratio indices formulated with two or more of these sensitive bands led to the identification of new leaf hopper indices (LHI) with a potential to detect leafhopper severity. These new indices along with 20 other stress related hyperspectral indices compiled from literature were further tested for their ability to detect LH severity. Two novel indices LHI 2 and LHI 4 proposed in this study showed significantly high coefficients of determination across locations (R² range 0.521 to 0.825^∗∗) and hence have the potential use for detection of leafhopper severity in cotton.     

Digital image analysis to estimate the live weight of broiler

Computer assisted digital image analysis was performed to investigate the possibility of estimating body weight of live broiler. To achieve the stated objective, 100 Arbor acres broiler chicks were reared under standard rearing condition and 1200 digital images were captured from 20 randomly selected broilers during the 7-42 days growing period. The captured images were analyzed by raster image analysis software (IDRISI 32) to determine the broiler body surface area and developed a linear equation to estimate weights of the broiler from its body surface-area pixels. The developed weight predicted equation based on surface-area pixels was log W = 1.060406(log P) + 0.173756(log A) − 2.029268 (W = estimated body weight, P = surface-area pixels and A = age at weighing) and the degree of goodness of fit of this equation was 0.999. The relative error in weight estimation of broiler chicken by image analysis, expressed in terms of percent error of the residuals from surface-area pixels was in between 0.04 and 16.47. On the other hand, the estimated body weights were not significantly (p > 0.05) difference from manually measured body weights up to 35 days of age. Thus, the development of a practical imaging system for weighing live broiler is feasible.     

Quantitative analysis of minerals and electric conductivity of red grape homogenates by near infrared reflectance spectroscopy

The use of near infrared (NIR) reflectance spectroscopy to measure the concentration of minerals and electric conductivity (EC) in red grape homogenates was investigated. Wine grape samples (n = 209) from two vintages, representing a wide range of varieties and regions were analysed by Inductively Coupled Plasma Optical Emission Spectrometry (ICPOES) for the concentrations of calcium (Ca), potassium (K), magnesium (Mg), phosphorus (P), sulphur (S), iron (Fe), and manganese (Mn) and scanned in reflectance in a NIR instrument (400-2500 nm). The spectra were pre-processed using multiple scatter correction (MSC) before developing the calibration models using partial least squares (PLS) regression and cross validation. Coefficients of determination in cross validation (R²) and the standard errors of cross validation (SECV) obtained were for Fe (0.60 and 1.49 mg kg⁻¹), Mn (0.71 and 0.41 mg kg⁻¹), Ca (0.75 and 60.89 mg kg⁻¹), Mg (0.84 and 12.93 mg kg⁻¹), K (0.78 and 285.34 mg kg⁻¹), P (0.70 and 40.19 mg kg⁻¹), S (0.88 and 14.45 mg kg⁻¹) and EC (0.87 and 7.66 mS). The results showed that Mg, S and EC in grape berries might be measured by NIR reflectance spectroscopy.     

Extended-altitude, aerial mapping of crop NDVI using an active optical sensor: A case study using a Raptor™ sensor over wheat

Recently reported testing of active, optical crop sensors in low-level aircraft have demonstrated a new class of airborne sensing system that can be deployed under any ambient illumination conditions, including at night. A second-generation, high-powered, light-emitting diode system has been assembled and tested over a 80 ha field of wheat (Triticum aestevum) by mapping the normalised difference vegetation index (NDVI) at altitudes ranging from 15 to 45 m above the canopy; significantly higher altitudes than existing systems. Comparisons with a detailed on-ground NDVI survey indicated the aerial sensor values were highly correlated to the on-ground sensor (0.79 < R² < 0.85), with close to unity slope and zero offset. The maximum average deviation between aerial and on-ground NDVI values was 0.04. Sample calculations involving two exemplar algorithms, one for estimating monoculture pasture biomass and the other for estimating wheat yield, indicate such deviations to have no significant effect on prediction accuracy. The subsequent NDVI maps proved to be invariant to sensor height over the 15-45 m altitude range meaning this new sensor configuration can be deployed over undulating crops and pastures and in areas with nearby obstacles such as trees and buildings.     

A web-based model for simulating whole-farm beef cattle systems

This paper details the rationale and procedures included in a web-based simulator containing production of beef cattle and cash crops. All biophysical and economical definitions including management in terms of rules constitute user-defined scenarios, which can be saved, retrieved, modified and deleted. Herbage growth may be either provided by the user or estimated from daily climate information. Scenarios are simulated and their outcomes are sent to the user's e-mail as a spreadsheet file, including data and pre-designed graphs to facilitate the interpretation of results. An example simulation study of a grazing-based finishing beef steers with a cycle of 12 months is presented, where new weaners enter the system in the autumn. Increased stocking rates, SR (150, 170, 190 and 210 heads in a 100-ha farm) and different summer maize feeding (SMF) rates were compared (SMF₀ = control and SMF₁ = intermittent rule-driven maize feeding during summer). In this simulation study, “deviations” from user-defined targets are simplified into two associated rules (a) average farm herbage mass deviation to adjust herbage allowance, and (b) target animal live weight deviation, associated to SMF₁. The proportion of unfinished steers at 28th February increased linearly (p ≤ 0.01) with higher stocking rates and became zero at any stocking rate where the SMF rule was operating (SMF₁). The economic response of SR × SMF interaction was significant for gross margin, indicating a positive linear association (p ≤ 0.05) of SMF₁ treatment (rule activated) when SR increases. The potential use of the web simulator as a flexible tool for investigation, training and decision making are discussed.     

The vegetation classification in coal mine overburden dump using canopy spectral reflectance

The canopy spectral characteristics of typical plants in the overburden of the Fuxin coal mine dump were measured and analyzed. The reflectance of Leymus chinensis was affected by the soil, with a slight shift from green (550 nm) to the near infrared (NIR) region. Changes in chlorophyll and water absorption were not significant in the red (670 nm) and NIR bands, respectively. The reflectance curve trend for Artemisia lavandulaefolia was similar to those of Sophora japonica and Ulmus pumila, while the reflectance of S. japonica and U. pumila fluctuated in the NIR region (760-1200 nm), especially with greater water absorption around 930 and 1120 nm. In contrast, the reflectance of A. lavandulaefolia fluctuated slightly around 930 nm and a significant peak appeared at 1127 nm. In addition, the spectral reflectance of S. japonica was lower than for the other species in the visible band (400-700 nm). However, it was higher than for L. chinensis in the NIR region (780-1200 nm). Three classifiers, the self-organizing map (SOM), learning-vector quantization (LVQ), and a probabilistic neural network (PNN), were used to classify the vegetation and the results of all classifiers were compared based on total spectral reflectance data from 400 to 1200 nm. The PNN was the best classifier in terms of training and testing accuracy. The first difference reflectance was calculated, and the red edge parameter was able to classify the herbs (L. chinensis and A. lavandulaefolia) and the arbores (S. japonica and U. pumila) with an accuracy of 77 and 84%, respectively, although it did not perform as well for detail species. A mixing parameter matrix was built based on the sensitive wavelengths (550, 674, 810, 935, and 1125 nm), the vegetation indices (SAVI and NDGI), and the water absorption slope. High classification accuracy was obtained by applying the mixing parameter matrix. This method could be used for revegetation monitoring and in decision making.     

Aerodynamic analysis and CFD simulation of several cellulose evaporative cooling pads used in Mediterranean greenhouses

The present work makes an aerodynamic analysis and computational fluid dynamics (CFD) simulation of the four commercial models of corrugated cellulose evaporative cooling pads that are most widely used in Mediterranean greenhouses. The geometric characteristics of the pads have been determined as well as the volume of water they retain at different flows of water, thus obtaining the mean thickness of the sheet of water which runs down them and their porosity. By means of low velocity wind tunnel experiments, the pressure drop produced by the pads has been recorded at different wind speeds and water flows. In this way it has been possible to obtain the relationship of the permeability and the inertial factor with pad porosity using a cubic type equation. Finally, a CFD simulation with a 3D model has been carried out for both dry pads (Q_w = 0 l s⁻¹ m⁻²) and wet ones (Q_w = 0.256 l s⁻¹ m⁻²), finding good correlation between the simulated and experimental pressure drop, with maximum differences of 9.08% for dry pads and 15.53% for wet ones at an airspeed of 3 m s⁻¹.     

Model fusion for prediction of apple firmness using hyperspectral scattering image

Hyperspectral scattering image is an advanced technology widely used in non-destructive measurement of fruit quality. To develop a better prediction model for apple firmness, the present study investigates a model fusion method coupled with wavelength selection algorithms. The current paper first discusses two wavelength selection algorithms, namely, uninformative variable elimination (UVE) and supervised affinity propagation (SAP). The selected effective wavelengths are then set as input to the partial least square (PLS) model. Six hundred “Golden Delicious” apples were analyzed. The first 450 apples were used as sample for the calibration model, whereas the remaining 150 were used for the prediction model. Compared with full wavelengths, the number of effective wavelengths based on the UVE and SAP algorithms decreased to 34% and 35%, but the correlation coefficient of prediction (Rp) increased from 0.791 to 0.805 and 0.814, whereas the root mean-square error of prediction (RMSEP) decreased from 6.00 to 5.73 and 5.71 N, respectively. A fusion model was then developed using UVE-PLS and SAP-PLS models coupled with backpropagation neural network. A better prediction accuracy was achieved from the fusion model (Rp = 0.828 and RMSEP = 5.53 N). The model fusion provides an effective modeling method for apple firmness prediction using hyperspectral scattering image technique.     

The Estimation of Soil Trace Elements Distribution and Soil-Plant-Animal Continuum in Relation to Trace Elements Status of Sheep in Huangcheng Area of Qilian Mountain Grassland,China

The purpose of the present study was to survey contents of trace elements of Cu, Mn, Fe, and Zn in the surface layer (0-20 cm) in the soil, pasture and serum of sheep in Huangcheng area of Qilian mountain grassland, China. Also the soil-plant-animal continuum was analyzed. Soil (n=300), pasture (n=60), and blood serum samples from sheep (n=480) were collected from Huangcheng area of Qilian mountain grassland, China. The contents of trace element in the samples were analyzed by atomic absorption spectrophotometer after digestion. The soil trace elements density distribution shows a ladder-like pattern distribution. Equations developed in the present study for prediction of Fe (R²=0.943) and Zn (R²=0.882) had significant R² values.     

基于MGWRK的土壤有机质制图及驱动因素研究

【目的】空间预测是一种获得有机质空间局部细节的重要方法,其准确性对于农田合理管理有着重要意义。本研究通过对比不同的土壤有机质空间制图方法以获得更优的预测精度,在预测的同时揭示环境协变量的空间非平稳性特征及不同环境协变量关系的空间尺度。【方法】选取晋中盆地的7个乡镇作为研究区,对比普通克里金（OK）、回归克里金（RK）、地理加权回归克里金（GWRK）和多重尺度地理加权回归克里金（MGWRK）4种不同方法对土壤有机质的预测能力和效果,并探究影响因子在空间分布中对有机质的影响效应变化和这种影响效应的空间尺度。MGWRK是一种多重尺度地理加权回归（MGWR）与普通克里金方法相结合的方法。【结果】选取坡向、坡度、年均降水量、年平均温度、海拔、植被年总初级生产力、年蒸散量、地形湿度指数、平面曲率、汇流动力指数、地形指数、地形粗糙指数、年平均NDVI为环境协变量参与建模,在多元线性回归建模中,模型统计学意义显著,这表明模型具备统计学意义。从Radius指数来看,各模型模拟效果由好到差依次为RK、OK、GWRK、MGWRK;从制图效果来看,MGWRK与GWRK制图效果相当,从有机质的空间预测图可以看出,土壤有机质在研究区呈现东西两侧偏低、中部偏高的空间格局,其中汾河以东、昌源河流经区域土壤有机质普遍偏高。坡向、年均降水量、年平均温度、海拔、地形指数、年平均NDVI对研究区东部有机质的影响强于西部,而坡度、植被年总初级生产力、年蒸散量、平面曲率、汇流动力指数、地形粗糙指数则表现出截然相反的空间非平稳性特征,地形湿度指数对有机质的影响则体现为北部强南部弱。【结论】MGWRK方法的空间预测精度分别达到了RK方法的69%、OK方法的71.74%、GWRK方法的71.17%。MGWRK在空间非平稳性特征的解释能力和空间可视化表现良好,为有机质的预测和空间非平稳性特征的描述提供方法借鉴。     

Evaluation of plant seedling water stress using dynamic fluorescence index with blue LED-based fluorescence imaging

A dynamic fluorescence image index system capable of non-destructive assessment of water stress in cabbage seedlings was developed. The quenching curves of chlorophyll fluorescence characteristic to the plant's water stress status under reduced excitation energy were acquired via a multispectral imaging system. The dynamic fluorescence index (DFI), a fluorescence index derived from quantitative modeling of the quenching curves in this study, was found superior to conventional indices such as the fluorescence decrease ratio (Rfd) in evaluating water stress status in cabbage seedlings, with respect to due mainly to its significantly reduced measuring time and enhanced precision of the chlorophyll fluorescence responses. The best results of the quantitative model to predict water stress status of seedlings using the dynamic fluorescence index were r = 0.944 and SEE = 0.286 MPa using the 720 nm channel. The blue (460 nm) LED-excited fluorescence multispectral imaging system (FMSIS) coupled with DFI provides a powerful research tool for plant physiology studies as well as for improving greenhouse and agriculture management practices where quantitative assessment of water potential in leafy plants is critical.     

The effects of step-wise improvement of forage combination in total mixed rations on fatty acid profile in the rumen and milk of Holstein cows

Five lactating Holstein cows in a 5×5 Latin square experiment were fed five high-concentrate total mixed rations(TMRs) to investigate the effects of step-wise improvement of forage combination on ruminal and milk fatty acid profiles. The ratio of concentrate to forage was fixed as 61:39, and the step-wise improvement of forage combination was applied as: TMR1, a ration containing corn stover; TMR2, a ration containing corn stover and ensiled corn stover; TMR3, a ration containing ensiled corn stover and Chinese wild ryegrass hay(Leymus chinensis); TMR4, a ration containing the ryegrass hay and whole corn silage; TMR5, a ration containing the ryegrass hay, whole corn silage and alfalfa hay. The TMRs were offered to the cows twice daily at 0700 and 1900 h. The entire experiment was completed in five periods, and each period lasted for 18 days. Diurnal samples of rumen fluids were collected at 0100, 0700, 1300 and 1900 h(day 16); 0300, 0900, 1500 and 2100 h(day 17); and 0500, 1100, 1700 and 2300 h(day 18). The step-wise improvement of forage combination increased energy and crude protein contents and decreased fibre content. As a result, the step-wise improvement of forage combination increased dry matter intake and milk yield(P0.05). The step-wise improvement increased dietary content of linolenic acid(C18:3 n-3), but did not alter dietary proportions of palmitic acid(C16:0), stearic acid(C18:0), oleic acid(C18:1 cis-9), linoleic acid(C18:2 n-6) and arachidic acid(C20:0). In response to the forage combination, ruminal concentration of C16:0, C18:2 n-6 and C18:3 n-3 linearly increased against their dietary intakes(P0.10). The step-wise improvement increased milk contents of C10:0, C12:0, C14:0, C16:0, C18:2 n-6 and C18:3 n-3(P0.10) and decreased milk contents of C18:0 and C18:1 cis-9(P0.05). Milk yields of C16:0, C18:1 cis-9, C18:2 n-6 and C18:3 n-3 were linearly increased by the increase of these fatty acids in the rumen(R2≥0.79, P0.05), and milk yields of C18:2 n-6 and C18:3 n-3 were also positively correlated with dietary intake of these fatty acids(R2≥0.85, P0.05). The step-wise improvement increased the transfer efficiencies from feed to milk for C18:2 n-6 from 11.8 to 14.2% and for C18:3 n-3 from 19.1 to 22.3%. In a brief, along with the step-wise improvement of forage combination, more dietary linoleic and linolenic acids might escape microbial hydrogenation in the rumen and consequently accumulated in milk fat though these fatty acids were present in low concentrations in ruminal fluids. The step-wise improvement of forage combinations could be recommended as a dietary strategy to increase the transfer efficiency of linoleic and linolenic acids from feed to milk.     

利用OSC算法消除土壤含水量变化对Vis-NIR光谱估算有机质的影响

【目的】快速、准确地监测土壤有机质对于精准农业的发展具有重要意义。可见光-近红外(visible and near-infrared,Vis-NIR)光谱技术在土壤属性估算、数字化土壤制图等方面应用较为广泛,然而,在田间进行光谱测量,易受土壤含水量(soil moisture,SM)、温度、土壤表面状况等因素的影响,导致光谱信息中包含大量干扰信息,其中,SM变化是影响光谱观测结果最为显著的因素之一。此研究的目的是探讨OSC算法消除其影响,提升Vis-NIR光谱定量估算土壤有机质(soil organic matter,SOM)的精度。【方法】以江汉平原公安县和潜江市为研究区域,采集217份耕层(0—20 cm)土壤样本,进行风干、研磨、过筛等处理,采用重铬酸钾-外加热法测定SOM;将总体样本划分为3个互不重叠的样本集:建模集S~0(122个样本)、训练集S~1(60个样本)、验证集S~2(35个样本);设计SM梯度试验(梯度间隔为4%),在实验室内获取S~1和S~2样本集的9个梯度SM(0%—32%)的土壤光谱数据;分析SM对土壤Vis-NIR光谱反射率的影响,采用外部参数正交化算法(external parameter orthogonalization,EPO)、正交信号校正算法(orthogonal signal correction,OSC)消除SM对土壤光谱的干扰;利用主成分分析(principal component analysis,PCA)的前两个主成分得分和光谱相关系数两种方法检验消除SM干扰前、后的效果;基于偏最小二乘回归(partial least squares regression,PLSR)方法建立EPO和OSC处理前、后的SOM估算模型,利用决定系数(coefficient of determination,R~2)、均方根误差(root mean square error,RMSE)和RPD(the ratio of prediction to deviation)3个指标比较PLSR、EPO-PLSR、OSC-PLSR模型的性能。【结果】土壤Vis-NIR光谱受SM的影响十分明显,随着SM的增加,土壤光谱反射率呈非线性降低趋势。OSC处理前的湿土光谱数据主成分得分散点相对分散,与干土光谱数据主成分得分空间的位置不重叠,不同SM梯度之间的光谱相关系数变化较大;OSC处理后的湿土光谱数据主成分得分空间的位置基本与干土光谱数据相重合,各样本光谱数据之间相似性很高,不同SM梯度之间的光谱相关系数变化较小。9个SM梯度的EPO-PLSR模型的验证平均R~2_(pre)、RPD分别为0.69、1.7。9个SM梯度的OSC-PLSR模型的验证平均R~2_(pre)、RPD分别为0.72、1.89,校正后的OSC-PLSR模型受SM的较小,有效提升SOM估算模型的精度和鲁棒性。【结论】OSC能够消除SM变化对土壤Vis-NIR光谱的影响,可为将来田间原位实时监测SOM信息提供一定的理论支撑。     

NIRS Prediction of SOM,TN and TP in a Meadow in the Sanjiang Plain,China

The aim of this study was to establish the applicability of near-infrared reflectance spectroscopy (NIRS) as a rapid method for the accurate estimation of nutrient components in agricultural soils. Focusing on the soil of the Sanjiang Plain, NIRS was used to predict soil organic matter (SOM), the total nitrogen (TN) and the total phosphorus (TP). A total of 540 samples were collected from the three different depths (180 samples from each depth: 0-10, 10-20 and 20-30 cm), from 2015 to 2017, from the Sanjiang Plain in Heilongjiang Province, China. From every depth, 120 samples were used to construct the calibration set. Other 60 samples were used to check the efficiency of the model. Combining the first-order differentiation with the partial least square (PLS) method, a prediction model was obtained to measure SOM, TN and TP. The correlation coefficient of SOM from 0 to 10 cm was R2=0.9567, from 10 to 20 cm was R2=0.9416, and from 20 to 30 cm was R2=0.9402. The corresponding ratio (standard deviation [SD]/root mean square error of prediction [RMSEP]) was >2.96. R2 of TN with the three depths was 0.9154, 0.9028 and 0.9024, respectively, all with SD/RMSEP>2.89. Meanwhile, R2 of TP with the three depths was 0.8974, 0.8624 and 0.7804, respectively, all with SD/RMSEP>2.50. These results demonstrated that NIRS based on the first-order differentiation and PLS could efficiently predict SOM, TN and TP from different soil depths.     

Monitoring nitrogen,phosphorus and sulphur in hybrid rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) using hyperspectral remote sensing

Till date, the remote sensing research on crop nutrient monitoring has focused mainly on biomass and nitrogen (N) estimation and only a few attempts had been made to characterize and monitor macronutrients other than N. Field experiments were undertaken to study the remote detection of macronutrient status of rice using hyperspectral remote sensing. The variability in soil available N, phosphorus (P) and sulphur (S) and their content in plants were created using artificial fertility gradient design. The leaf and canopy hyperspectral reflectance was captured from variable macronutrient status vegetation. Linear correlation analysis between the spectral reflectance and plant nutrient status revealed significantly (p < 0.05) higher correlation coefficient at 670, 700, 730, 1090, 1260, 1460 nm for the nutrient under study. Published and proposed vegetation indices (VIs) were tested for canopy N, P and S prediction. The results of the investigation revealed that, published VIs (NDVI hyper and NDVI broadbands) could retrieve canopy N with higher accuracy, but not P and S. The predictability of the visible and short wave infrared based VI NRI₁₅₁₀ ((R₁₅₁₀ ? R₆₆₀)/(R₁₅₁₀ + R₆₆₀)) was the highest (r = 0.81, p < 0.01) for predicting N. Based on the outcomes of linear correlation analysis new VIs were proposed for remote detection of P and S. Proposed VI P_670_1260 ((R₁₂₆₀ ? R₆₇₀)/(R₁₂₆₀ + R₆₇₀)) retrieved canopy P status with higher prediction accuracy (r = 0.67, p < 0.01), whereas significantly higher canopy S prediction (r = 0.58, p < 0.01) was obtained using VI S_670_1090 ((R₁₀₉₀ ? R₆₇₀)/(R₁₀₉₀ + R₆₇₀)). The proposed spectral algorithms could be used for real time and site-specific N, P and S management in rice. Nutrient specific wavelengths, identified in the present investigation, could be used for developing relatively low-cost sensors of hand-held instruments to monitor N, P and S status of rice plant.     

Sensors for detecting and logging spatial distribution of urine patches of grazing female sheep and cattle

Urine excreted in patches by grazing livestock accounts for much of the nitrogen (N) loss to the environment in grazing systems. These losses arise mainly through N leaching to ground water and gaseous losses to the atmosphere. Models of grazing systems ideally require data on urination frequency, N load in each urination event and spatial distribution patterns of urine. To date there has been a lack of suitable equipment for obtaining information on urination characteristics of grazing animals. This paper describes a urine sensor that detects and logs each urination event of female sheep and cattle. A thermistor suspended below the vulva continuously measures ambient temperature except when urine passes over it, causing the temperature to rise to near body temperature. Field validation showed 85% and 78% of sensor-detected urination events by sheep and cattle sensors, respectively, were seen by the observer. Some of the urination events identified by the sensor but not seen by the observer were confirmed by video footage. For cattle, only one observed urination event was not detected by the urine sensor, giving a 4% error rate of detection. Daily urination frequencies (ewes: 13-23 events day⁻¹; cows: 11-26 events day⁻¹) were similar to published data for these species. A custom-made GPS unit worn on the rump of the ewe and on the collar of the cow logged animal position continuously so that urine patch position could be determined. Examples of urine distribution patterns by ewes and beef cows in large, hilly paddocks clearly show campsite locations where a disproportionate number of urination events occurred. For sheep, the correlation between time spent in an area and the number of urination events in the same area was r = 0.82, but this correlation was weaker for cattle (r = 0.54).     

谷子苗期氮高效品种筛选及相关特性分析

【目的】评价不同基因型谷子苗期氮素吸收利用差异性,筛选谷子氮高效利用基因型材料,为谷子氮高效利用品种选育和机理研究提供理论依据。【方法】采用沙培盆栽试验,以具有代表性生态类型的79个谷子品种为材料,分析其在低氮(0.2 mmol·L~(-1))和高氮(6 mmol·L~(-1))处理下茎叶干物重、含氮量、氮素吸收量、氮素吸收与利用效率的差异及相关性,并划分不同生态类型品种的氮效率类型。【结果】供试谷子品种在2个氮素水平条件下的茎叶干物重(CV_(N0.2) 35.39%和CV_(N6) 50.83%)、氮素含量(CV_(N0.2) 11.52%和CV_(N6) 11.22%)、氮素吸收量(CV_(N0.2) 32.82%和CV_(N6) 48.46%)、氮素吸收效率(CV_(N0.2) 32.82%和CV_(N6) 48.45%)、氮素利用效率(CV_(N0.2) 11.53%和CV_(N6) 11.27%)和氮效率(CV_(N0.2) 35.35%和CV_(N6) 50.61%)均存在较大差异。不同生态类型谷子品种的氮素吸收和利用效率差异显著,西北春谷类型氮素吸收效率的变化(CV_(N0.2) 39.99%和CV_(N6) 54.38%)显著高于华北夏谷类型(CV_(N0.2)29.31%和CV_(N6) 45.68%)和东北春谷类型(CV_(N0.2) 29.49%和CV_(N6) 40.30%),而氮素利用效率以华北夏谷类型品种间差异最大(CV_(N0.212.03%和CV_(N6) 12.70%)。茎叶干物重与氮素吸收和氮素利用效率呈极显著正相关(P0.01),相关系数分别为R~2_(N0.2)=0.1827**和R~2_(N6)=0.1027**及R~2_(N0.2)=0.8985**和R~2_(N6)=0.9442**;氮效率与氮素吸收量极显著正相关,与氮含量极显著负相关,相关系数分别为R~2_(N0.2)=0.8985**和R~2_(N6)=0.9442**及R~2_(N0.2)=0.1962**和R~2_(N6)=0.0998**;氮素利用效率与氮含量极显著负相关,相关系数分别为R~2_(N0.2)=0.9924**和R~2_(N6)=0.9910**。氮素吸收效率与氮素含量和氮素利用效率间无显著相关性。以两氮素水平条件下茎叶干物重和氮效率的平均值为标准,将3种生态类型的谷子品种划分为4种氮效率类型,双高效型、双低效型、高氮高效型和低氮高效型。其中,东北春谷双高效型和高氮高效型品种所占比重最高(P_(东北)52.9%P_(西北)36.0%P_(华北)29.7%和P_(东北)23.5%P_(华北)18.9%P_(西北)4.0%),双低效型比重最低(P_(东北)17.6%P_(华北)32.4%P_(西北)36.0%),而低氮高效型在西北春谷类型中所占比重最高(P_(西北)24.0%P_(华北)18.9%P_(东北)5.9%)。【结论】不同谷子品种苗期氮效率差异显著,且西北春谷类型品种间氮素吸收效率差异最大,华北夏谷类型品种间氮素利用效率差异最大;氮素吸收效率和利用效率之间无显著相关性,应作为2个独立的氮效率指标进行评价和改良。