茶园土壤中氟去除模型研究

在单因素试验基础上,采用二次回归正交旋转组合设计对土壤中的氟去除进行了优化,建立了土壤氟潜在去除率(y)与EDTA浓度(x1)、溶液pH值(X2)、表面活性剂(SDS)投加量(x3)和土壤含氟量(x4)4个因素间的正交回归模型:Y=62.92642-6.80471x1+2.85102x2+3.74368x3-6.65557x12-4.74638x42+2.70625x1x4+2.70625x2x3.从模型推知,当EDTA浓度0.085 mol/L、溶液pH值8.9、SDS投加量21.70 mL和土壤含氟量650.65 mg/kg时,土壤氟潜在去除率最大,达71.70%,验证结果与模型值相近.     

作物需水量模拟计算结果有效性检验

作物需水量是确定节水高效灌溉制度、制定灌溉排水规划和水资源合理配置必不可少的重要参数。当前模拟计算作物需水量的主要方法有FAO-56双作物系数法、双涌源能量守恒模型、根系层水量平衡模型和SWAP模型,而各种方法均有利弊和使用条件。不论采用哪种方法模拟计算作物需水量,其结果必须进行有效性检验,否则不能用于上述工程项目。该文用FAO-56双作物系数法基于灌溉试验数据模拟计算了人工牧草——老芒麦、冰草的需水量,并对模拟计算的需水量采用拟合相关图法、回归分析法和残差估计误差指示法进行了有效性检验。拟合相关图法属定性检验,给出了统计相关趋势;回归分析法和残差估计误差指示法为定量检验,给出了模拟计算值与实测值间的拟合优度和残差估计误差的范围。这种定性定量相结合的方法有效地检验了需水量模拟值与其实测值间的一致性,其结果可用于工程项目中。     

氮肥施用对砖红壤硝态氮和盐基离子淋失特征的影响

氮肥品种的合理选用对作物增产增收、 土壤酸化改良有重要的影响。本文以海南省海口市观澜湖采集的砖红壤为研究对象,采用室内土柱模拟试验,对尿素、 硝酸铵和硫酸铵3种氮肥处理下砖红壤硝态氮及盐基离子（Ca2+、 Mg2+、 K+、 Na+）淋失特征进行了研究。结果表明, 1）硝态氮累积淋溶量表现为硫酸铵硝酸铵尿素N0,且硝态氮的淋溶量与施肥量呈正相关关系,整个淋溶过程中硝态氮累积淋溶量（y kg/hm2）与施肥量（x kg/hm2）之间满足线性方程 y=3.3064x+315.74（R2=0.8848）; 2）尿素、 硝酸铵、 硫酸铵处理整个淋溶过程的盐基离子淋溶量均表现为 Ca2+Mg2+K+Na+,且盐基离子淋溶总量（kg/hm2）表现为硫酸铵（1821.12）硝酸铵（1080.27）尿素（872.24）N0（417.23）; 3）砖红壤盐基离子迁移速率表现为硫酸铵（26.28%）硝酸铵（13.37%）尿素（11.78%）,尿素、 硝酸铵和硫酸铵处理分别以线性方程 y=0.1178x+123.18（R2=0.9121）、 乘幂方程 y=15.634x0.4423（R2=0.9259）和对数方程 y=128.38e0.0007x（R2= 0.9244）的拟合度最高。     

氮肥运筹对水稻农学效应和氮素利用的影响

通过田间试验,以不同氮肥量级为参照,结合关键生育期叶片叶绿素含量（SPAD值）指导氮肥施用,以探明潜江地区水稻关键生育期的氮肥适宜用量。结果表明,在施N 90～180kg/hm2间水稻产量差异不显著,当超过N 180 kg/hm2,产量降低。根据水稻产量（y）和施氮量（x）拟合得出一元二次关系式:y = -0.0728x2 + 22.335x + 6811.5,R2 = 0.9442。结合当年水稻价格肥料投入费用等计算出水稻的经济效益（Y）和施氮量（X）之间的函数式:y = -0.134x2 + 37.097x + 12533-M,R2 = 0.9331;由此得出经济效益最大时水稻的施氮量是N 138 kg/hm2。该施氮量下水稻的氮肥表观利用率,农学利用率和氮肥偏生产力可保持在40.9%,11.5 kg/ kg和63.2 kg/ kg,与完全依据SPAD值指导关键生育期的氮肥施用量相近似（N 140 kg/hm2）,保证了水稻最大的经济效益,同时也保持了较高的氮肥利用率,降低氮素表观损失。     

黄腐酸吸附土壤Cr6+的模型研究

在单因素实验的基础上,针对黄腐酸(FA)吸附土壤中的Cr6+的研究,采用了二次回归正交旋转组合设计对其实验条件进行了优化,并建立了土壤Cr6+潜在去除率(y)与FA浓度(x1)、溶液pH值(x2)、反应时间(x3)和反应温度(x4)这4个因素间的正交回归模型。从模型推知,当在FA浓度为2.11g/L,溶液pH值为5.65,反应时间为8.8h和反应温度为23.8℃时,土壤Cr6+潜在去除率最大,达78.27%,验证结果表明,实验结果与模型结果较为吻合。     

冻融期土壤温度对有机污染物迁移行为的影响

为了解季节性冻融期土壤垂直分层温度变化对有机污染物迁移扩散行为的影响。通过冬季大田试验分别测定了裸地,5,10,15cm厚度秸秆覆盖条件下3,10,20,40,60,100cm深度土壤温度,并应用土—气交换层及相邻土壤层迁移扩散通量系数,深入研究了土壤温度变化对有机污染物迁移行为的影响,并系统地分析了不同温度数据对模拟结果产生的差异性。研究结果表明:不同覆盖条件下深层土壤有机氯农药α-HCH迁移扩散通量系数数值的变化与土壤层厚度有关;土壤垂直分层温度的变化与α-HCH的迁移扩散通量系数呈显著正相关,且随着土壤深度的增加相关性逐渐减弱;日平均气温变化与土—气交换层α-HCH的迁移扩散通量系数的变化具有明显的一致性(R=0.999,p=0.000);日平均气温与土壤垂直分层温度观测数据建立的回归方程能够较好地与0—40cm土壤深度实测温度相吻合:y=-0.788+0.818x(3cm,R=0.964,p0.01),y=-1.214+0.705x(10cm,R=0.942,p0.01),y=0.912+0.474x(20cm,R=0.836,p0.01),y=1.004+0.361x(40cm,R=0.714,p0.01);40cm深度土壤预测温度计算α-HCH迁移扩散通量系数产生的相对误差小于使用日平均气温作为替代数据的计算结果。本研究结果将可能为有机污染物在土壤中的迁移扩散行为及相关数值模拟研究提供参考价值。     

红壤侵蚀坡地上百喜草的施肥效应和合理施肥技术

在红壤侵蚀坡地小区试验条件下,进行百喜草N、P、K肥效试验和单施N肥效应研究,结果表明:在供试土壤区域,促进百喜草生长及鲜草量的最优施肥方案为氮肥单施,施用氮肥的曲线回归模型为y=b0 b1x b2x2-b3x3,效应方程为y=51000.0 275.5995x 3.3750x2-0.1335x3,合理的氮肥用量范围为189.0~598.5 kg/hm2,经济最佳氮肥施用量为250.5 kg/hm2。     

松材线虫病病死木数量回归预测研究

为科学进行松材线虫病防治规划并精准制定治理经费预算,开展松材线虫病精细化预测预报技术研究。选择安徽省潜山市25个发病小班,使用2018年松褐天牛蛀孔数与2019年度所在小班病死树数量建立回归模型,并使用该模型对2020年度数据进行预测和预测精度对照。结果表明,建立的回归模型为y=0.595 601+53.325 250x;25个小班中,2020年病死木预测值与实际值误差小于10%的小班数为20个,占比为80%,预测误差小于5%的小班数为13个,占比为52%,该模型的预测结果具有较高的预测精度。     

虹鳟稚鱼培育实验研究

对虹鳟稚鱼体长和体重的生长情况进行观察,对虹鳟稚鱼培育过程中整齐度进行了研究。试验结果表明:①虹鳟稚鱼体重日平均增为50mg,并得体重-时间方程y=0.005x+0.1669(R2=0.9854)。②体长日平均增长0.04cm,体长-时间方程y=0.004x+2.21(R2=0.8972)。经检验,稚鱼生长整齐度无显著性差异。     

乙草胺对土壤脲酶动力学特征的影响

采用室内模拟试验方法,以典型棕壤为供试土壤,研究不同浓度乙草胺对土壤脲酶活性和酶动力学参数的影响。结果表明,不同浓度乙草胺能显著抑制土壤脲酶活性,并在培养的第3～6 d达到最大抑制;利用模型 y=c/(1+bx)和y=c(1+ax)/(1+bx) 对不同浓度乙草胺与土壤酶活性的关系进行拟合,证明模型y=c/(1+bx)拟合效果较好,表明乙草胺除草剂对土壤脲酶的抑制作用为完全抑制,而脲酶ED50 为13.12～75.76 mg/kg;乙草胺的施入使土壤脲酶Vmax值降低,Km值则保持不变,属于典型的非竞争性抑制。     

地形对漫川漫岗黑土区大豆产量的影响

为研究黑土区田块尺度地形对大豆产量造成的影响,在海伦东兴合作社具有明显地形起伏的地块,采集大豆田间试验数据,考虑温度、太阳辐射、坡度、土壤养分等因素,运用作物生长模型DSSAT(Decision Support Systemfor Agrotechnology Transfer)模型对各样点进行参数率定及验证,得出以下结论:1)DSSAT模型的模拟产量与实际产量的相对均方根误差为7.9%,模拟结果表现为优,表明运用作物模型模拟不同地形上的产量变异具有可行性;2)地形通过影响作物生长环境因子的时空差异决定产量差异,田块尺度温度、水分和坡度是影响产量差异的主要因素;3)坡顶和坡底的产量相对较高,且产量变异性较小,阳坡虽然接收到更多的光照,却由于水分胁迫造成减产,坡底和平缓坡顶水肥保持较好,易获得高产。研究成果为田间精细管理与田块尺度耕地高效利用提供科学依据。     

苹果品种用于加工鲜榨汁的适宜性评价

为科学评价苹果品种的鲜榨汁加工适宜性,以122个单果质量在100 g以上的品种为对象,运用相关分析、因子分析、概率分级、层次分析、K-均值聚类、判别分析等方法建立苹果品种鲜榨汁加工适宜性评价技术。结果表明：果实与鲜榨汁间可滴定酸含量、可溶性固形物含量、可溶性糖含量、固酸比和糖酸比5项指标均呈极显著相关,相关系数分别为0.8967、0.9393、0.8413、0.9036和0.9099。果实可溶性固形物含量、固酸比、出汁率、单宁含量等4项指标被确定为苹果品种鲜榨汁加工适宜性评价指标。4项指标均划分为服从或近似服从正态分布的5级,即极低、低、中、高和极高。根据指标权重和指标分级标准,建立了4项指标的评分标准。建立的苹果品种鲜榨汁加工适宜性判别函数有极高的判别准确性,正确判别率达到94.74%（建模样本）和96.55%（检验样本）。筛选出的58个优良品种中,红富士、乔纳金、津轻等43个品种适于加工鲜榨汁,澳洲青苹、红玉、金冠等15个品种极适于加工鲜榨汁。     

Comparing simulated crop yields with observed and synthetic weather data

Stochastic weather generators have been used in the development of climate scenarios which are input to agricultural simulation models that assess the climate impacts on crop growth and production. The synthetic data generated by a stochastic weather generator only mimic the observed weather data, thus discrepancies between the synthetic and the observed weather data often exist. For example, interannual variability in the synthetic data is often found to be weaker than in the observed data, i.e., the common problem of overdispersion. Here, we evaluate if the climate impact models are sensitive to such discrepancies. A stochastic weather generator (AAFC-WG) was used to generate 300 years long synthetic weather data for five Canadian locations, based on observed weather data for the baseline period of 1961−1990. The Decision Support System for Agrotechnology Transfer (DSSAT) v4.0 was employed to simulate crop growth and yield. Five major crops were simulated by the DSSAT model for three major soil types at each location, with 30-yr observed data and 300-yr synthetic data as weather input, respectively. Statistical tests were performed to investigate whether differences (both in mean and variance) of the simulated crop yields between the simulations with observed and synthetic weather data were statistically significant or not. Results showed that the differences in simulated crop yields were not statistically significant when synthetic weather data were used to substitute the observed data. Standard deviations of crop yield and biomass in simulations with synthetic weather data were, in 5 and 19% of all cases, respectively, found to be smaller by more than 20% to those simulated with observed weather. However, with only one exception, the differences in variances were not statistically significant. We conclude that reliable crop yield estimates can be obtained by combining the AAFC weather generator with the DSSAT crop growth models at the studied sites in Canada.     

Analysis of nitrogen fractions in silage by near-infrared spectroscopy

Total nitrogen, soluble nitrogen (SN), nonprotein nitrogen (NPN), and acid-detergent insoluble nitrogen (ADIN) were analyzed in grass silage by near-infrared (NIR) spectroscopy. A set of 144 samples was used to calibrate the instrument by modified partial least-squares regression, and the following statistical results were achieved: standard error of calibration (SEC) = 0.449 and square correlation coefficient (R (2)) = 0.98 for total nitrogen x 6.25, SEC = 0.425 and R (2) = 0.95 for SN x 6.25, SEC = 0.414 and R (2) = 0.94 for NPN x 6.25, and SEC = 0.139 and R (2) = 0.84 for ADIN x 6.25. To validate the calibration performed, a set of 48 silage samples was used. Standard errors of prediction were 0.76, 0.64, 0.63, and 0.25 for total nitrogen, SN, NPN, and ADIN (all of them multiplied by 6.25), respectively, and R (2) for the regression of measurements by reference method versus NIR analysis were 0.94, 0.92, 0.90, and 0.48 for total nitrogen, SN, NPN, and ADIN, respectively. To compare the results obtained by NIR spectroscopy with those obtained by the reference methods for total nitrogen, SN, and NPN of the validation set, linear regression and paired t tests were applied, and the results were not significantly different (p = 0.05). When mean square prediction error analysis was applied, it could be concluded that for total nitrogen, SN, and NPN, a robust calibration model was obtained and that the main error was unexplained error. Statistical data for ADIN were worse than those of the other parameters; as a result NIR spectroscopy is not an effective method for quantitative analyses of ADIN in silage; nevertheless, it may be an acceptable method for semiquantitative evaluation.     

Prediction of spatial soil property information from ancillary sensor data using ordinary linear regression: Model derivations, residual assumptions and model validation tests

Geospatial measurements of ancillary sensor data, such as bulk soil electrical conductivity or remotely sensed imagery data, are commonly used to characterize spatial variation in soil or crop properties. Geostatistical techniques like kriging with external drift or regression kriging are often used to calibrate geospatial sensor data to specific soil or crop properties. More traditional statistical methods such as ordinary linear regression models are also commonly used. Unfortunately, some soil scientists see these as competing and unrelated modeling approaches and are unaware of their relationship. In this article we review the connection between the ordinary linear regression model and the more comprehensive geostatistical mixed linear model and describe when and under what conditions ordinary linear regression models represent valid spatial prediction models. The formulas for the ordinary linear regression model parameter estimates and best linear unbiased predictions are derived from the geostatistical mixed linear model under two different residual error assumptions; i.e., strictly uncorrelated (SU) residuals and effectively uncorrelated (EU) residuals. The theoretically optimal (best linear unbiased) and computable (linear unbiased) predictions and variance estimates derived under the EU error assumption are examined in detail. Statistical tests for detecting spatial correlation in LR model residuals are also reviewed, in addition to three LR model validation tests derived from classical linear modeling theory. Two case studies are presented that highlight and demonstrate the various parameter estimation, response variable prediction and model validation techniques discussed in this article.     

Testing the adequacy of measured data for evaluating nitrogen turnover models by the dot-to-dot method

Dynamic models of nitrogen turnover in soil are being used increasingly in agricultural science. To be of value, a model must be thoroughly evaluated, and the expected accuracy of simulated values must be defined. Frequent field measurements are time‐consuming and costly, and so models are often evaluated against only few data. However, this mismatch between the measured and simulated values can result in error statistics that are themselves subject to large errors and therefore unreliable. The dot‐to‐dot method quantifies the error associated with having too few measured values by interpolating linearly between measured data (hence, dot‐to‐dot) and evaluating the simulation at each time step using interpolated data where actual measurements are unavailable. A large error will be seen if the measured data do not capture the fluctuations predicted by the model. Other quantitative methods can be used to evaluate the performance of a model, but the dot‐to‐dot method can be used in conjunction with these to estimate whether or not the data are adequate for testing the model. If the performance of the model at the measured points is within the acceptable error, then the dot‐to‐dot method is used to judge whether there are too few points to evaluate the model's performance, and so to determine whether the evaluation of the model is valid.     

中亚热带丝栗栲群落物种-个体关系的研究

对中亚热带丝栗栲群落物种 个体关系研究结果表明 ,剩余标准差 (RSE)、偏差绝对值平均值 (AAD)和相对偏差绝对值平均值 (AARD) 3个评价指标之间存在极强正秩相关 ,表明它们对物种 个体关系模型评价结果具很强一致性 ,乔木层 5种曲线拟合效果最佳为y =1.85 18x2 - 7.84 86x 14 .5 ,灌木层 5种曲线拟合效果最佳为y =12 4 84e0 .1572x;群落中乔木层和灌木层物种多度分布均遵从对数正态分布 ,表明该丝栗栲群落发展较成熟 ,稳定性和生物多样性较高     

基于不同估算方法的贵州省土壤温度状况

土壤温度状况是土壤系统分类重要的土壤诊断特性,是土壤某些分类单元的划分依据。以贵州省86个气象站点(1951/71—1980)地面气候资料为基础,应用不同的土壤温度估算方法(土温内插法、土温直接估算、纽荷模型估算、气温回归估算法、纬度海拔回归估算法),判定贵州省各县(市)的土壤温度状况。结果表明,5种方法的估算结果基本一致,"不同海拔的贵州省经纬度海拔回归估算"方法在贵州省土壤温度状况估算中的应用更为广泛。贵州省土壤温度状况包括温性、热性和高热性三种土壤温度状况类型;有80个县(市)的估算结果属于热性土壤温度状况;威宁、大方属于温性土壤温度状况;有4个县(市)估算结果处在不同土壤温度状况临界值附近,存在两种土壤温度状况,其中水城、开阳和习水存在温性和热性两种土壤温度状况,而罗甸则存在高热性和热性两种土壤温度状况。将贵州省土壤温度状况作为诊断特性应用于土壤系统分类时,应综合考虑成土环境条件。     

棉(Gossypium hirsutum L.)纤维发育相关酶和纤维比强度对棉铃对位叶氮浓度变化的响应研究

于2008～2009年在长江流域下游棉区,选用纤维比强度差异明显的德夏棉1号（平均比强度26.2 cN/tex）、科棉1号（平均比强度35 cN/tex）和美棉33B（平均比强度32 cN/tex）为试验材料,设置不同施氮量以形成不同的棉铃对位叶氮浓度,研究了棉铃对位叶氮浓度对纤维发育过程中关键酶（蔗糖酶、蔗糖合成酶、磷酸蔗糖合成酶、-1, 3-葡聚糖酶）活性及纤维比强度形成的影响。结果表明,棉铃对位叶氮浓度随施氮量的增加而上升,随花后天数的变化符合幂函数方程YN＝t-［YN:棉铃对位叶氮浓度（%）;t:花后天数（d）;、为参数］。在花后同一时期,纤维发育关键酶活性和纤维比强度均随棉铃对位叶氮浓度的上升呈先升后降的趋势,可用抛物线方程Y=ax2+bx+c拟合［Y:酶活性或纤维比强度（cN/tex）;x:叶片氮浓度（%）;a、b、c为参数］。表明在纤维发育过程中,棉铃对位叶氮浓度显著影响纤维中相关酶活性和纤维比强度的形成,各指标所对应的最佳棉铃对位叶氮浓度差异较小;因此,通过调节对位叶氮浓度可调控相关酶活性达到最优以及棉花高强纤维的形成。在本试验条件下,中部棉纤维发育所需的最佳对位叶氮浓度动态变化方程分别为:NDexiamian1=7.2841t-0.2771(R2=0.9860＊＊);NKemian1=7.1807t-0.2989(R2=0.9879＊＊);NNuCOTN33B=7.1467t-0.2819(R2=0.9755＊＊)。     

基于MA-ARIMA-GASVR的食用菌温室温度预测

食用菌温室温度具有时变、非线性、多耦合特性,准确预测对稳定食用菌生产具有重要意义。本研究从挖掘温室历史温度数据时序信息角度出发,提出一种MA-ARIMA-GASVR组合方法建立温度预测模型,利用移动平均方法将历史温度序列分解成线性序列和残差序列,然后采用移动平均差分自回归模预测线性序列的趋势,再将移动平均差分自回归预测值、历史残差数据、历史温度数据作为支持向量回归模型的输入,并结合遗传算法优化支持向量回归模型参数改善其性能,从而获得更符合实际情况的温度预测值。最后选取实测温度数据作为训练集,对未来2d的温度进行预测验证。结果显示,MA-ARIMA-GASVR组合方法能更好地拟合原始温度数据,间隔1h的均方误差、平均绝对误差和平均绝对百分误差分别为0.18、0.36和1.34,均显示本研究方法预测精度优于支持向量回归、遗传算法优化的支持向量回归单一模型,也优于未经移动平均以及未经遗传算法优化的组合模型;此外,间隔6h的均方误差、平均绝对误差和平均绝对百分误差为0.29、0.52和1.95,说明本研究方法还能满足6 h以内的多步预测,为食用菌生产者预留更多调整时间。