中红外光谱技术在牛奶营养物质预测及奶牛相关特性分析上的应用

近年来,许多研究表明采用中红外光谱(MIRS)分析技术能够对牛奶中各营养物质(如脂肪酸、蛋白质和矿物质)及潜在的有害物质(如掺杂物、抗生素)进行实时、快速和准确地定量分析,并以此建立预测模型来对奶牛的营养(如饲料转化率、能量利用效率和甲烷排放)、健康(如乳房炎和代谢性疾病)和生殖生理与繁殖状况进行鉴定、评估和筛选,从而为优化畜牧养殖业生产方式,发展创新畜牧养殖业循环经济模式,实现低碳、健康和可持续发展提供有效的技术保障。因此本文主要就近年来国际上最新的关于MIRS分析技术在牛奶营养物质测定及奶牛相关特性分析方面的研究进展进行综述。     

Soil organic matter fractionation as a tool for predicting nitrogen mineralization in silty arable soils

After decades of searching for a practical method to estimate the N mineralization capacity of soil, there is still no consistent methodology. Indeed it is important to have practical methods to estimate soil nitrogen release for plant uptake and that should be appropriate, less time consuming, and cost effective for farmers. We fractionated soil organic matter (SOM) to assess different fractions of SOM as predictors for net N mineralization measured from repacked (disturbed) and intact (undisturbed) soil cores in 14 weeks of laboratory incubations. A soil set consisting of surface soil from 18 cereal and root‐cropped arable fields was physically fractionated into coarse and fine free particulate OM (coarse fPOM and fine fPOM), intra‐microaggregate particulate OM (iPOM) and silt and clay sized OM. The silt and clay sized OM was further chemically fractionated by oxidation with 6% NaOCl to isolate an oxidation‐resistant OM fraction, followed by extraction of mineral bound OM with 10% HF (HF‐res OM). Stepwise multiple linear regression yielded a significant relationship between the annual N mineralization (kg N/ha) from undisturbed soil and coarse fPOM N (kg N/ha), silt and clay N (kg N/ha) and its C:N ratio (R² = 0.80; P < 0.01). The relative annual N mineralization (% of soil N) from disturbed soils was related to coarse fPOM N, HF‐res OC (% of soil organic carbon) and its C:N ratio (R² = 0.83; P < 0.01). Physical fractions of SOM were thus found to be the most useful predictors for estimating the annual N mineralization rate of undisturbed soils. However, the bioavailability of physical fractions was changed due to the disturbance of soil. For disturbed soils, a presumed stable chemical SOM fraction was found to be a relevant predictor indicating that this fraction still contains bio‐available N. The latter prompted a revision in our reasoning behind selective oxidation and extraction as tools for characterizing soil organic N quality with respect to N availability. Nonetheless, the present study also underscores the potential of a combined physical and chemical fractionation procedure for isolating and quantifying N fractions which preferentially contribute to bulk soil N mineralization. The N content or C:N ratio of such fractions may be used to predict N mineralization in arable soils.     

Soil Properties Degradation Assessment Using Infrared Scanning Technique of Soils （Case Study： Eastern of Congo）

Recently, near infrared reflectance （NIR） and mid-infrared （MIR） spectroscopy techniques are increasingly introduced as convenient and simple non-destructive techniques for quantifying several soil properties. This study uses MIR method to predict pH, soil organic C, total N, AI, Ca, Mg and K, CEC and soil texture for soil samples collected in Sud-Kivu, Congo. A total of 536 composite soil samples were taken from two locations （Burhale and Luhihi） at two depths （0-20 cm and 20-40 cm） using a spatially-stratified random sampling design within an area of 200 km2. Differences in characteristics were evaluated between the two locations, land use （cultivated vs. non-cultivated land） with soil depths. A random subset of the samples （10%） were analyzed using standard wet chemistry methods, and calibration models developed by MIR data to estimate soil properties for the full soil sample set. Partial least squares regression （PLS） method gave acceptable coefficients of determination between 0.71 and 0.93 for all parameters. Soil organic matter levels were higher in cultivated plots in Luhihi （3.9% C） than in Burhale （3.0% C）, suggesting lower levels of soil fertility in the later area. This indicates high levels of acidity, which are likely to limit crop production in the area. Phosphorus deficiency is acute in Burhale （2.4 mg P/kg） but less in Luhihi （5.4 mg P/kg）. In both locations, low levels of Ca and Mg indicate that soils may be susceptible to deficiencies in both elements.These findings provide new opportunities for monitoring soil quality in the region which can benefit multiple actors and scientists involved in the agricultural and environmental sectors.     

Prediction of daily reference evapotranspiration by a multiple regression method based on weather forecast data

Prediction of daily reference evapotranspiration (ET ₀) is the basis of real-time irrigation scheduling. A multiple regression method for ET ₀ prediction based on its seasonal variation pattern and public weather forecast data was presented for application in East China. The forecasted maximum temperature (T _max), minimum temperature (T _min) and weather condition index (WCI) were adopted to calculate the correction coefficient by multilinear regression under five time-division regimes (10 days, monthly, seasonal, semi-annual and annual). The multiple regression method was tested for its feasibility for ET ₀ prediction using forecasted weather data as the input, and the monthly regime was selected as the most suitable. Average absolute error (AAE) and root mean square error (RMSE) were 0.395 and 0.522 mm d^?1, respectively. ET ₀ prediction errors increased linearly with the increase in temperature prediction error. A temperature error within 3 K is likely to result in acceptable ET ₀ predictions, with AAE and average absolute relative error (AARE) <0.142 mm d^?1 and 5.8%, respectively. However, one rank error in WCI results in a much larger error in ET ₀ prediction due to the high sensitivity of the correction coefficient to WCI and the large relative error in WCI caused by one rank deviation. Improving the accuracy of weather forecasts, especially for WCI prediction, is helpful in obtaining better estimations of ET ₀ based on public weather data.     

Corn (Zea mays L.) Grain and Stover Yield as Affected by Soil Residual Mineral Nitrogen

Evaluation of nitrogen (N) dynamic in soil using regression equations is important for proper determination of N fertilization. A 3-year field experiment was conducted to (1) develop the best-fitted regression model relating corn grain and stover yield to soil residual ammonium (NH₄)-N and nitrate (NO₃)-N for corn yield prediction and (2) evaluate how such a model can be beneficial to the health of ecosystem by predicting the appropriate rates of N fertilization for corn production. Soil NH₄-N and NO₃-N were determined at corn harvest at the depths of 0–30 and 30–60 cm. Nitrogen fertilizer rates and soil mineral N accounted for a maximum of 93% variation in corn grain yield. Soil mineral N enhanced corn yield more than N fertilizer. Totals of 63.1 and 14.1 kg/ha of soil residual NO₃-N and NH₄-N were found in the 0- to 60-cm depth, indicating the importance of performing soil N tests.     

不同性别龙胜凤鸡生长曲线的拟合与分析

为了解龙胜凤鸡的生长发育规律，本研究运用Logistic、Gompertz和Bertalanffy3种非线性模型分别对龙胜凤鸡的生长情况进行曲线拟合和分析。研究结果表明，3种模型均能较好的模拟龙胜凤鸡生长曲线，拟合度假。）均高于0．99，其中以Bertalanffy模型的拟合度最高（公、母鸡都为0．997）。因此，运用3种模型对龙胜凤鸡进行生长曲线的拟合和比较分析是可行的，本研究也为及时了解龙胜凤鸡的生长发育规律提供了参考。     

环青海湖地区草地蝗虫发生测报的气候指标研究

采用次序统计量方法,以1988～1999年环青海湖地区草地蝗虫发生程度资料和气候资料研究了蝗虫灾害等级与当地气候指标的定量关系,建立了春季和夏季预测模型,并以此对2000～2003年该地区蝗虫发生状况进行了预测试验。结果表明,敏感气候因子有:上年11～12月气温、3～6月温度及5～6月降水量等。春季预测模型中的指标为:(1)T11<-6℃;(2)T12<-10℃;(3)T3>-4℃;(4)T4>2℃。夏季预测模型中的指标为:(1)(T11+T12)/2<-8.5℃;(2)(T3+T4)/2>-1.5℃;(3)(T5+T6)>7.5℃;(4)(R5+R6)<48mm。当模型中有3个或4个指标满足,则当年可能发生严重蝗虫灾害,当没有或只有1～2个指标满足,则不会发生严重蝗虫灾害。     

华北棉田二代棉铃虫发生程度中期预测

在华北棉区用扫网法和目测查虫法调查麦田一代棉铃虫幼虫发生量，用比较法和生命表分析法预测二代棉铃虫在棉田的发生程度和虫口密度。经1991—1994年应用检验，预测结果均与实际发生情况吻合。     

反刍动物采食量的概念与研究方法

本研究对反刍家畜采食量的研究发展与现状进行了回顾与总结,采食量及其相关术语的定义主要基于饲草料或家畜,其测定方法和精确含义有差异;反刍家畜采食是由生物和非生物因素相互作用共同影响的复杂动态过程,采食量影响因子主要分为家畜的(胃肠蠕动力、选择性采食等)、饲草料的(粗纤维含量、抗营养成分等)和饲养条件的(草层结构、草地饮水点分布等)因素3个方面;采食量的测定方法有基于牧草测定的直接法和基于家畜测定的间接法,后者较多地应用于放牧家畜;预测方法有基于数学函数和生物学原理的模型法,基于消化试验的经验法和结合家畜属性的改进经验法。舍饲条件下可准确地测定和预测家畜的采食量,但对放牧家畜还没有精确的方法。通过测定与饲草水平密切相关的家畜尿液、血液或粪便中的代谢物水平将成为预测放牧家畜采食量的准确方法。