Determination of critical nitrogen dilution curve based on leaf area index for winter wheat in the Guanzhong Plain,Northwest China

Excessive use of nitrogen(N) fertilizers in agricultural systems increases the cost of production and risk of environmental pollution. Therefore, determination of optimum N requirements for plant growth is necessary. Previous studies mostly established critical N dilution curves based on aboveground dry matter(DM) or leaf dry matter(LDM) and stem dry matter(SDM), to diagnose the N nutrition status of the whole plant. As these methods are time consuming, we investigated the more rapidly determined leaf area index(LAI) method to establish the critical nitrogen(N_c) dilution curve, and the curve was used to diagnose plant N status for winter wheat in Guanzhong Plain in Northwest China. Field experiments were conducted using four N fertilization levels(0, 105, 210 and 315 kg ha-1) applied to six wheat cultivars in the 2013–2014 and 2014–2015 growing seasons. LAI, DM, plant N concentration(PNC) and grain yield were determined. Data points from four cultivars were used for establishing the N_c curve and data points from the remaining two cultivars were used for validating the curve. The N_c dilution curve was validated for N-limiting and non-N-limiting growth conditions and there was good agreement between estimated and observed values. The N nutrition index(NNI) ranged from 0.41 to 1.25 and the accumulated plant N deficit(N_(and)) ranged from 60.38 to –17.92 kg ha~(-1) during the growing season. The relative grain yield was significantly affected by NNI and was adequately described with a parabolic function. The N_c curve based on LAI can be adopted as an alternative and more rapid approach to diagnose plant N status to support N fertilization decisions during the vegetative growth of winter wheat in Guanzhong Plain in Northwest China.     

基于卷积神经网络的拖拉机工况识别

农机工况识别在细化农机作业状态和帮助掌握区域污染物排放趋势方面有着重要的研究价值。基于拖拉机不同运行状态下的行驶速度、发动机转速以及实时油耗等时间序列,首次提出将图像识别方法引入到拖拉机工况识别中的思路,并分别应用参数优化的支持向量机与卷积神经网络对实际作业拖拉机工况进行研究。结果表明:(1)基于参数优化的支持向量机可以较好地实现样本点的工况识别且识别准确度达到99.851 9%,但无法实现农机工况的连续性识别,同时无法对农机工况转换阶段进行有效识别。(2)以拖拉机运行速度与发动机转速等信息构建样本图像来描述农机工况变化的数据表达,并在此基础上应用卷积神经网络可以有效实现农机工况的连续性识别,且识别准确率可以达到93.3%。本研究在农机工况识别方面具有一定参考价值,并为后续农机不同工况下区域污染物排放研究提供技术支持。     

玛沁县畜牧业现状及可持续发展对策

通过对玛沁县畜牧业发展现状的分析，总结多年来的经验，提出了畜牧业可持续发展的策略。     

阔叶树种的研究现状与江西研究的发展方向

通过对阔叶树种在我国及江西省的研究状况的论述，初步总结了阔叶树种的研究水平及研究方向，指出了江西省阔叶树种研究存在的问题，探讨了江西省今后阔叶树种研究的方向。     

谷物冷却机回风利用与节能

分析了谷物冷却机采用回风型冷却的节能原理及优点，回风型冷却具有重要的节能意义。     

丰本生力酵素饲喂肉猪试验研究

选用60日龄杜洛克、长白仔猪，Ⅰ组为试验组，Ⅱ组为对照组。对照组饲养基础日粮，试验组在对照组的基础上添加1％的丰本生力酵素。结果表明：平均日增重试验组均高于对照组，其增长幅度杜洛克为13．1％，长白猪为8．12％。饲料转化率差异不显著，但瘦肉率试验组比对照组提高了13．1％。经济效益明显提高，杜洛克试验组比对照组提高了4．8％，长白猪试验组比对照组提高了5．3％。     

东北地区外来针叶树种引种现状及发展策略

介绍了东北林区主要针叶树种引种现状及取得的成就，分析了东北三省针叶树种引种工作中存在的问题，提出了建立较为科学的引种网络体系和发展策略，并提供了可能在干旱、半干旱区引种成功的针叶树种名     

齐卡商品肉兔在不同饲养条件下的育肥效果

作者在不同条件下，采用全窝育肥法，重复两年、两个季节测定了齐卡商品肉兔的育肥效果；探讨了温度、季节、营养水平、育肥始重对育肥效果的影响。结果表明：在试验场育肥２５０只齐卡商品兔，９０日龄育肥重２．３３～２．５３公斤，日均增重３１克以上，耗料指数３．３１：１，育肥成活率９５％以上，屠宰率５２％，净肉率８０％，肉骨比５．３５：１，兔肉品质正常。在农村条件下育肥１８０只，９０～１００日龄育肥重２．２～２．４公斤，耗精料指数２．５５：１，育肥成活率８８％～９３％。饲养齐卡商品兔与饲养二元杂交兔相比，每只可增加经济收入２０％以上。育肥季节和始重明显影响育肥未重（Ｐ＜０．０５）。依据本试验，作者建议齐卡商品兔适宜３５日龄断奶，育肥始重应不低于６００克；寒冷季节育肥，育肥第一月的室温应不低于１０℃。育肥适宜营养水平建议消化能（ＤＥ）２６００～２７００大卡／公斤，粗蛋白（ＣＰ）１８％，粗纤维（ＣＦ）１２％以上。     

Lactate Kinetics in veterinary Critical Care: A Review

Elevation in blood lactate concentration, with or without accompanying metabolic acidosis, is a hallmark finding in patients with circulatory compromise, and is also consistently noted in other conditions affecting critically ill or injured individuals. Little is reported in a veterinary literature regarding lactate measurement in the emergency and critical care setting, despite impressive reports of the clinical usefulness of lactate measurement in people. The purpose of this article is to review lactate kinetics and the clinical utility of lactate measurement. Limitations to lactate evaluation will also be discussed.     

五种豆科牧草耐盐临界值、极限值的研究

针对黄淮海２０００ｋｍ２内陆盐碱地，经２年时间，用盆栽实验方法进行研究，确定出五种豆科牧草耐盐临界值和极限值。其中保定苜蓿、猎人河苜蓿、红豆草这三种豆科牧草耐盐临界值为０．４％，极限值为０．５％。草木樨耐盐临界值为０．３％，极限值为０．４％。沙打旺耐盐临界值０．２％，极限值为０．３％。