不同玉米品种的产量和营养价值评价

为了筛选出适宜当地种植的粮饲通用型玉米品种，推进临洮县玉米生产向粮改饲方向调整。以先玉335为对照，对12个玉米品种的生物产量、籽粒产量和营养品质进行比较。结果表明，陇单703、5291生物产量和籽粒产量均较高，干物质产量分别为20.04、17.70 t/hm2，鲜草产量分别为60.16、59.64 t/hm2，籽粒产量分别为16.08、17.74 t/hm2；青贮营养品质也较优，粗蛋白含量分别为99.4、85.9 g/kg，中性洗涤纤维含量分别为408.3、430.5 g/kg，酸性洗涤纤维含量分别为238.8、247.0 g/kg，淀粉含量分别为353.0、359.1 g/kg，均达到国际一级品种要求。以上2个品种可作为粮饲通用型玉米品种在临洮县推广种植。     

基于SPOT-5遥感影像估算玉米成熟期地上生物量及其碳氮累积量

通过构建基于SPOT\|5光谱参数的玉米成熟期地上生物量、 碳氮累积量的遥感估算模型,为耕地生产力估测、 农田生态保护和碳氮循环研究提供依据。利用皮尔逊相关分析法分析玉米成熟期地上生物量、 碳氮累积量与同期14个预选光谱参数之间的相关性,筛选出适宜的光谱参数;通过回归分析,比较得出最优遥感估算模型。在构建的14个光谱参数中,土壤校正植被指数（SAVI）与玉米成熟期地上生物量和碳累积量均呈显著的正相关,相关系数分别达到0.831和0.846,因此以SAVI为底数的幂函数模型估算生物量和碳累积量的拟合效果最好,决定系数（R2）分别达到0.698和 0.722,在0.01水平下的F检验均呈显著性;与氮累积量相关性最强的是由近红外波段和绿波段构建的比值指数（R3/R1）,相关系数达到0.844;从而以R3/R1为自变量的线性模型对氮累积量拟合效果最佳,决定系数（R2）达到0.713,在0.01水平下的F检验呈显著性。因此,利用SPOT\|5的土壤校正植被指数（SAVI）、 近红外波段和绿波段的比值指数（R3/R1）构建的遥感模型来估算玉米成熟期生物量、 碳氮累积量是可行的。     

不同品种燕麦秸秆体外发酵产气特性的研究

采用改进的体外发酵产气技术评价8种不同燕麦品种秸秆的体外产气特性,分析并讨论不同品种燕麦秸秆发酵动力学参数的差异及其与营养成分之间的相关关系。结果表明,不同品种燕麦秸秆发酵动力学参数存在显著差异(P<0.05)。理论最大产气量(A)和产气速率(b)以B a iY an N o.4最高,极显著高于F 004和M eng Y an H-8631(P<0.01),A显著高于Y ou Y an N o.1(P<0.05);发酵延滞时间以B a iY an N o.4最短,而以M engY anH-8 6 3 1最长;ZaoY anN o.2和F 0 0 1的发酵液pH值显著高于0 7 0 4 6-1 8-1-2-3和B etty-1的;其他燕麦秸秆间差异不显著。相关分析表明,12、24、48 h产气量及产气速率(b)与中性洗涤可溶物减去灰分(NDS-A sh)及中性洗涤可溶物减去灰分后再减去粗蛋白质(NDS-A sh-CP)含量呈显著正相关,而与中性洗涤纤维(NDF)和酸性洗涤纤维(ADF)含量呈显著负相关;理论最大产气量(A)仅与NDS-A sh及NDF-A sh-CP含量呈显著(P<0.05)正相关关系;而发酵延滞时间(LAG)则与中性洗涤纤维(NDF)含量呈显著正相关,而与NDS-A sh及NDF-A sh-CP含量呈显著负相关(P<0.05)。燕麦秸秆中的NDF及NDF-A sh-CP含量是影响燕麦秸秆发酵产气特性的主要因素。     

青贮玉米与大豆秸秆混合比例及添加剂对青贮品质及微生物数量的影响

为明确晋北地区农牧交错带青贮玉米（Zea mays）与大豆（Glycine max）秸秆混合比例及添加剂对青贮品质及微生物数量的影响，以全株玉米和大豆秸秆为材料，按质量比10∶0(T0)、8∶2(T2)、6∶4(T4)、4∶6(T6)、2∶8(T8)、0∶10(T10)进行混合青贮，每个混合比例分别设无添加剂对照组（CK）和添加布氏乳杆菌（LB）两个处理，测定青贮饲料的营养成分、发酵品质及微生物数量，并进行感官评定。结果表明，除单独青贮大豆秸秆外，其他处理组的青贮饲料感官评定均为优良。随着大豆秸秆比例的增加，干物质、粗蛋白、水溶性碳水化合物含量增加，中性洗涤纤维和酸性洗涤纤维含量逐渐下降。青贮玉米和大豆秸秆混合比例为4∶6和2∶8时，乳酸含量高于其他处理组，而pH值、乙酸、丙酸含量和氨态氮/总氮低于其他处理组，且使用添加剂能改善饲料的青贮品质。同时，当青贮玉米和大豆秸秆以4∶6和2∶8的比例混合时，可形成养分互补，提高饲用价值，实现秸秆资源利用价值最大化。本研究为确定晋北地区农牧交错带混合青贮的最优混合比例提供了理论依据。     

近红外光谱法预测羊肉化学成分的研究

对3个品种、3个部位的106个羊肉样品进行近红外光谱扫描,并测定其蛋白质、水分、脂肪含量,采用Unscrambler软件建立基于偏最小二乘法的近红外光谱预测模型。结果显示:样品水分含量近红外光谱校正决定系数为0.94,验证决定系数是0.86;蛋白质含量近红外光谱预测模型的校正决定系数为0.90,验证决定系数为0.72;脂肪含量近红外光谱校正决定系数0.81,验证决定系数0.64,由此可知近红外光谱用于羊肉品质检测具有可行性。本研究为羊肉化学成分的快速检测提供了基础。     

贮备饲料近红外光谱模型快速预测青绿饲料营养成分含量

为快速检测饲料的营养成分,该研究利用贮备饲料的近红处技术(near-infrared,NIR)快速分析模型预测青绿饲料的营养成分含量。基于贮备饲料的NIR定标模型,将建模优化模式转移应用到青绿饲料的营养成分定量检测,以判断模型转移能力。在实验室环境下扫描并记录新鲜的青绿饲料样本和储存的贮备饲料样本的近红外反射光谱,利用230个贮备饲料样本进行光谱定标训练,以修正偏最小二乘(modified-partial least squares,M-PLS)建模方法,结合随机局部样本、局部选参、局部非连续性可调、交叉检验等技术相结合的方式建立局部优化模型,分别测试120个贮备饲料样本和120个青绿饲料样本中的氮(nitrogen,N)、中性洗涤纤维(neutral detergent fiber,NDF)、酸性洗涤纤维(acid detergent fiber,ADF)含量。将贮备饲料的定标校正模型应用于贮备饲料验证样本的营养成分测定,其标准误差(square error of prediction,SEP):N为1.02、NDF为16.56和ADF为13.47,相关系数均在0.9以上,相对预测偏差(relative prediction derivation,RPD)均大于3;该模型具有对青绿饲料样本的营养成分预测能力,其预测SEP:N为0.90、NDF为14.11和ADF为9.98,预测相关系数均在0.9以上,预测RPD均大于3,达到快速检测误差标准。由于局部建模过程中考虑了数据的潜在非线性结构和具有近似光谱响应的样本之间的不均匀性,相对全局建模方式而言具有更好的数据驱动性质,其建模效果优于全局建模方法。结果表明,基于贮备饲料样本建立的NIR定标校正模型可以用于青绿饲料营养成分的预测,特别是局部分析模型的应用能够提高NIR快速分析的预测精度。     

Sentinel-2 MSI和Landsat 8 OLI数据在玉米秸秆覆盖度遥感估算应用中的比较研究

秸秆覆盖度（Crop residue cover, CRC）的遥感估算可以在短时间内获取大范围耕地秸秆覆盖度数据,对于政府部门监测保护性耕作的实施情况有重要的现实意义。本研究基于Sentinel-2 MSI和Landsat 8 OLI数据,分别计算了多种光谱指数,并与野外实测的秸秆覆盖度数据进行相关性分析,挑选出极显著性相关的光谱指数。在此基础上,构建其与秸秆覆盖度之间的相关模型,并通过决定系数（R2）和均方根误差（RMSE）所表征模型的精度比较Sentinel-2 MSI和Landsat 8OLI数据由于光谱和空间尺度的差异对秸秆覆盖度反演模型的影响。结果表明：6种光谱指数与CRC的相关性系数均大于0.4,相关性较高的是Sentinel-2 MSI 20 m分辨率数据获取的NDTI和STI,相关系数分别为0.878、0.894,相关性最低的为Sentinel-2 MSI 10 m分辨率数据获取的NDSVI,相关系数为0.476;利用一元线性回归法构建模型时,Sentinel-2 MSI 20 m分辨率数据构建的光谱指数STI和NDTI,模型精度最高,R2分别为0.810和0.800,...     

日粮精粗比对泌乳奶牛养分消化的影响

选择4头安装有永久性瘤胃、十二指肠前端和回肠末端T形瘘管的荷斯坦泌乳奶牛,按4×4拉丁方设计,分别以铬染纤维和Co-EDTA为食糜固相和液相指示剂,研究精粗比约为30∶70的“高低质粗料型”日粮、30∶70的混合型高青贮日粮、50∶50的精粗料比例相当的日粮及65∶35的高精料日粮等4种日粮对瘤胃食糜稀释率及干物质(dry matter,DM)、有机物(organic matter,OM)、中性洗涤纤维(neutraldetergent fiber,NDF)和酸性洗涤纤维(acid detergent fiber,ADF)等养分进食量、十二指肠流量及消化道不同部位表观消化率的影响。结果表明:日粮精粗比显著(P<0.05)或极显著(P<0.01)影响奶牛DM、OM、NDF和ADF进食量、十二指肠流量及DM、OM和ADF瘤胃表观降解率,而对奶牛瘤胃食糜液相和固相稀释率、NDF瘤胃表观降解率及DM、OM、NDF和ADF后肠道和全消化道表观消化率没有影响(P>0.05)。     

内蒙古东部中晚熟区青贮玉米产量和品质与气象因素的关系

为研究青贮玉米(Zea mays L.)产量和品质与气象因素的关系,于2018—2019年在通辽市农业科学研究院试验基地,以北农青贮368、东科301、京科青贮516及京科968为材料,设置D1(4月24日)、D2(5月1日)、D3(5月8日)和D4(5月15日)4个播期,比较不同播期条件下青贮玉米产量和品质的变化,分析青贮玉米产量和品质形成与气象因素的关系。结果表明,随着播期的推迟,各品种青贮玉米生育期缩短,株高和茎粗均呈增加趋势,绿叶数变化不大,D2播期的穗位较高。各品种在D2播期的平均生物产量最高(35.2～40.2 t·hm^-2)。随着播期推迟,中性洗涤纤维(NDF)和酸性洗涤纤维(ADF)含量呈增加趋势,粗蛋白含量变化不明显,D2播期淀粉含量较高。生育期日照时数(R²=0.381,P=0.031)和吐丝后日照时数(R²=0.507,P=0.003)与干物质产量呈负相关,生育期降雨量(R²=0.591,P<0.001)和吐丝后降雨量(R²=0.367,P=0.03...     

近红外光谱技术定量分析玉米杂交种纯度

摘要：应用近红外光谱分析技术结合定量偏最小二乘法对先玉335杂交种纯度进行了定量分析,将不同年份和来源的杂交种和其母本种子粉碎后混合,按0.5%的梯度获得纯度80～100%范围内的样本123份（每梯度按年份和来源设置3个重复）后采集光谱。结果表明：采用散射校正预处理,4 000～8 000 cm-1光谱范围时建模效果较适宜（建模集∶检验集=3∶1）,建模集内部交叉决定系数达96.06%,校正标准差1.18%,平均相对误差1.03%;检验集的决定系数均达到95.02%,校正标准差1.28%,平均相对误差1.12%。采用不同比例的建模样品和检验样品时,建模集和检验集的决定系数均在94%以上,证明了近红外光谱技术定量测定玉米杂交种纯度的可行性以及所建模型的稳定性。     

Rapid prediction of acid detergent fiber, neutral detergent fiber, and acid detergent lignin of rice materials by near-infrared spectroscopy

A rapid predictive method based on near-infrared spectroscopy (NIRS) was developed to measure acid detergent fiber (ADF), neutral detergent fiber (NDF), and acid detergent lignin (ADL) of rice stem materials. A total of 207 samples were divided into two subsets, one subset (approximately 136 samples) for calibration and cross-validation and the other subset for independent external validation to evaluate the calibration equations. Different mathematical treatments were applied to obtain the best calibration and validation results. The highest coefficient of determination for calibration (R2) and coefficient of determination for cross-validation (1-VR) were 0.968 and 0.949 for ADF, 0.846 and 0.812 for NDF, and 0.897 and 0.843 for ADL, respectively. Independent external validation still gave a high coefficient of determination for external validation (r2) and a low standard error of performance (SEP) for the three parameters; the best validation results were SEP = 0.933 and r2 = 0.959 for ADF, SEP = 2.228 and r2 = 0.775 for NDF, and SEP = 0.616 and r2 = 0.847 for ADL, indicating that NIR gave a sufficiently accurate prediction of ADF and ADL content of rice material but a less satisfactory prediction for NDF. This study suggested that routine screening for these forage quality parameters with large numbers of samples is possible with NIRS in early-generation selection in rice-breeding programs.     

New NIRS calibrations for fiber fractions reveal broad genetic variation in Brassica napus seed quality

Near-infrared reflectance spectroscopy (NIRS) calibrations were developed for the estimation of neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL) in intact seeds of oilseed rape ( Brassica napus ). A set of 338 diverse winter oilseed rape genotypes showing broad variation for seed color was used as a basis for the new calibrations. Different calibrations were generated for 10 or 1 mL seed volumes, respectively. In both seed volumes good coefficients of determination for external validation (R(2)) of the calibrations were obtained for ADL, the major antinutritional fiber fraction in oilseed rape meal, and adequate calibrations for NDF and ADF. Evaluation of diverse B. napus germplasm with the new calibrations revealed a surprisingly broad variation in contents of ADL in dark-seeded oilseed rape. The ability to use NIRS for efficient selection of low-fiber genotypes, irrespective of seed color, represents an important breakthrough in breeding for improved nutritional quality of seed extraction meals from oilseed rape.     

Determination of acid-detergent fiber and crude protein in forages by near-infrared reflectance spectroscopy: collaborative study

A collaborative study was conducted to determine the standard error of difference among laboratories for near-infrared reflectance spectroscopic (NIRS) determination of acid-detergent fiber (ADF) and crude protein in forages. The 6 participating laboratories were members of the USDA/ARS National Near-Infrared Reflectance Spectroscopy Forage Research Project. The NIRS calibration equations were developed in the Associate Referee's laboratory for crude protein and ADF and were transferred to the instrument in each of the other collaborating laboratories. The calibration set included over 650 diverse forage samples with crude protein and ADF calibration data; the validation set included 94 samples of bermudagrass. Among-laboratory reproducibility for the NIRS method, calculated as the relative standard deviation for reproducibility (RSDR), was 1.14% for ADF and 0.42% for crude protein. The variance component for among-laboratory variation (coefficient of variation) was 2.54% for ADF and 2.89% for crude protein. These results confirm that it is possible to calibrate, validate, and transfer (NIRS) equations and data among laboratories for the accurate determination of ADF and crude protein, and thereby demonstrate that NIRS can be used as a standard method for the analysis of forages. The method has been adopted official first action.     

Applicability of near infrared spectroscopy as an alternative to acid detergent analysis for cattle and swine manure compost

Abstract

Recently, acid detergent analysis has been reported to provide valid data to evaluate decomposition properties and to determine the available nitrogen (AVN) of organic materials, such as compost. However, this methodology requires complex procedures and creates considerable costs. As an alternative, near infrared spectroscopy (NIRS) was evaluated as a simple method to determine acid detergent fiber (ADF), acid detergent lignin (ADL) and acid-detergent-soluble organic matter (ADSOM), in order to evaluate the decomposition properties of cattle and swine manure compost. To establish an easy and accurate method of estimating AVN in cattle and swine manure compost, the accuracies of direct estimations of AVN by NIRS in incubation experiments and indirect estimations by NIRS based on acid-detergent-soluble nitrogen (ADSN) or total nitrogen (TN) were examined. The reflectance spectra of freeze-dried and milled compost samples were determined using a scanning monochromator. Second derivative spectra and multiple regression analysis were used to develop calibration equations for each constituent. The calibration equations for ADF, ADL and ADSOM were “successful” according to commonly applied criteria. Acid-detergent-soluble nitrogen was found to be more suitable than TN for estimating AVN in cattle and swine manure compost. As the accuracies of the estimations of ADSN and TN by NIRS were comparable, the estimation of AVN based on ADSN as determined by NIRS was more accurate than that based on TN determined by NIRS. The direct prediction of AVN through NIRS was not as accurate as the estimation of AVN based on ADSN determined by NIRS. We conclude that NIRS is a practicable alternative to the time-consuming acid detergent analysis of cattle and swine compost, and that ADSN as determined by NIRS is useful for estimating AVN in the compost.     

Applicability of near infrared spectroscopy as an alternative to acid detergent analysis for cattle and swine manure compost

Recently, acid detergent analysis has been reported to provide valid data to evaluate decomposition properties and to determine the available nitrogen (AVN) of organic materials, such as compost. However, this methodology requires complex procedures and creates considerable costs. As an alternative, near infrared spectroscopy (NIRS) was evaluated as a simple method to determine acid detergent fiber (ADF), acid detergent lignin (ADL) and acid-detergent-soluble organic matter (ADSOM), in order to evaluate the decomposition properties of cattle and swine manure compost. To establish an easy and accurate method of estimating AVN in cattle and swine manure compost, the accuracies of direct estimations of AVN by NIRS in incubation experiments and indirect estimations by NIRS based on acid-detergent-soluble nitrogen (ADSN) or total nitrogen (TN) were examined. The reflectance spectra of freeze-dried and milled compost samples were determined using a scanning monochromator. Second derivative spectra and multiple regression analysis were used to develop calibration equations for each constituent. The calibration equations for ADF, ADL and ADSOM were "successful" according to commonly applied criteria. Acid-detergent-soluble nitrogen was found to be more suitable than TN for estimating AVN in cattle and swine manure compost. As the accuracies of the estimations of ADSN and TN by NIRS were comparable, the estimation of AVN based on ADSN as determined by NIRS was more accurate than that based on TN determined by NIRS. The direct prediction of AVN through NIRS was not as accurate as the estimation of AVN based on ADSN determined by NIRS. We conclude that NIRS is a practicable alternative to the time-consuming acid detergent analysis of cattle and swine compost, and that ADSN as determined by NIRS is useful for estimating AVN in the compost.     

Winter annual small grain forage potential. II. Quantification of nutritive characteristics of four small grain species at six growth stages

Abstract

Four small grain species, barley, oat, rye, and wheat were harvested at six growth stages to investigate their nutritive quality as a possible replacement for corn silage. Crude protein (CP), in vitro dry matter disappearance (IVDMD), neutral detergent fiber (NDF), acid detergent fiber (ADF) and lignin contents were measured on fresh and ensiled samples. The IVDMD, an estimate of forage ruminant digestibility, of all species generally decreased from the vegetative (765–854 g kg^‐1) through the milk stage (505–662 g kg^‐1) and then remained unchanged or increased slightly through hard dough with the exception of oats which often decreased during later stages of growth. The IVDMD of rye was usually lower than the other species from the milk to hard dough stage. The IVDMD of barley was generally higher than the other species at the soft and hard dough stages. The NDF, ADF, and lignin fractions usually increased from the vegetative to milk stages and remained unchanged or increased slightly through the hard dough stage. The ADF and lignin are negatively associated with forage digestibility while NDF values are negatively related to dry matter intake. The ADF of rye was generally higher than the other species at the milk and soft dough stages. Rye and barley contained more lignin than the other species from the heading through hard dough stage. Crude protein content generally decreased from the vegetative through milk stages and then leveled off or decreased slightly through the hard dough stage. Crude protein of oats was lower than other species at the vegetative stage, while rye generally contained more crude protein than the other species at the vegetative and boot stages. These data in combination with forage yield data reported in an associated paper suggest that small grains can offer a nutritious source of animal feed for farmers in the southeastern United States. Rye tended to stand out among the species at the early stages of growth (vegetative to boot) as a highly digestible green chop or grazing crop that is also high in protein. Barley and wheat stand out as excellent high yielding, nutritious silage choices at the soft dough stage.     

Near-infrared reflectance spectroscopy (NIRS) for protein, tryptophan, and lysine evaluation in quality protein maize (QPM) breeding programs

Quality protein maize (QPM) has approximately twice the tryptophan (Trp) and lysine (Lys) concentrations in protein compared to normal maize. Because several genetic systems control the protein quality of QPM, it is essential to regularly monitor Trp and/or Lys in breeding programs. Our objective was to examine the potential of near-infrared reflectance spectroscopy (NIRS) to enhance the efficiency of QPM research efforts by partially replacing more expensive and time-consuming wet chemistry analysis. More than 276 maize samples were used to develop NIRS models for protein content (PC), Trp, and Lys. The standard error of prediction (SEP) for the calibration and the coefficient of determination for validation (R(2)(v)) were 0.26 and 0.96 for PC, 0.005 and 0.85 for Trp, and 0.02 and 0.75 for Lys. When the NIRS models were used to evaluate 266 S2 lines from five QPM breeding populations, the coefficients of determination between NIRS and the chemical data were 0.94, 0.76, and 0.80 for PC, Trp, and Lys, respectively. Therefore, the NIRS models can be used to support the QPM breeding efforts.     

Application of Near‐Infrared Reflectance Spectroscopy to Estimate Chemical Constituents in Broiler Litter

Abstract

Near‐infrared reflectance spectroscopy (NIRS) has potential to provide rapid estimates of phosphorus (P) and nitrogen (N) concentrations in broiler litter to assist managers in establishing application rates of litter to grazing lands that fall within productive and environmentally safe levels. An experiment was conducted to determine accuracy of NIRS estimates of moisture, P, N, and acid detergent fiber (ADF) concentrations in broiler litter. Broiler litter samples were collected from various farms to develop sample sets that were either with or without bedding material, and each sample set was subdivided into processed (i.e., dried and ground) and unprocessed samples to develop local equations for each constituent. Equations were developed by using all samples from each set and using samples following random removal of 20% of total for equation validation. Moisture was determined to be accurately measured by using NIRS based on a high R² (≥0.96), low SEC (<10 g kg^?1), and high s_x/SECV (>5.0). ADF also had a high R² (0.96), but the S_x/SEC (3.00) value was too low for the equation to be considered truly accurate. Estimations of P and N by calibrations that included all samples had a moderate to high R² values, but estimations for the validation set were relatively low in R² (≤0.78) and S_x/SEC (≤2.00). Concentrations of P and N were not estimated by NIRS with a high degree of accuracy, but other methodologies could enhance the usefulness of this technology to rapidly provide these nutrient measures.