景谷县森林蓄积量遥感估测及其动态变化分析

以云南省景谷县为研究区,基于Landsat5和Landsat8遥感数据,利用2012年和2017年两期全国森林资源连续清查实地调查数据,建立多元逐步回归和随机森林模型对景谷县森林蓄积量进行遥感估测对比研究。结果显示:综合建模精度来看,随机森林法在相同样地数量条件下具有更好的估测效果;从估测结果与林地保护利用规划和林地变更数据结果相比较,随机森林法估测值接近真实值,估测精度较高。     

琼岛杨一年生播种苗生长节律研究

于2019年5月6日到12月25日对海南省琼岛杨Populus qiongdaoensis一年生播种苗的苗高、地径生长量进行定期观测,采用Logistic方程进行生长进程拟合,研究琼岛杨的苗期生长节律。结果表明,琼岛杨苗高和地径生长都呈现"慢—快—慢"的"S"形生长规律,且苗高、地径生长量与Logistic方程的回归相关性达到极显著水平,其相关系数分别达0.994和0.996。苗高生长速生期约119 d,占生长期的39.67%,占苗高年总生长量的63.77%;地径生长速生期约125 d,占生长期的45.42%,占地径年总生长量的66.15%。     

长期施肥处理对双季晚稻叶绿素荧光特征及籽粒产量的影响

研究施肥模式对水稻叶绿素含量及荧光参数的调节作用,对提高水稻光能利用效率及籽粒产量具有重要意义。本研究以水稻品种赣929为试验材料,比较长期定位施磷钾肥(PK)、施氮磷钾肥(NPK)、等养分条件下70%氮磷钾肥配合施用30%有机肥(70F+30M)、50%氮磷钾肥配合施用50%有机肥(50F+50M)、30%氮磷钾肥配合施用70%有机肥(30F+70M)条件下水稻叶绿素荧光参数和产量变化特征。结果表明,水稻旗叶叶绿素含量从齐穗期到成熟期呈下降趋势,且表现为30F+70M50F+50M70F+30MNPKPK。水稻叶绿素荧光参数Fv/Fm随水稻生育期的推进呈降低趋势,其中PK处理Fv/Fm值最低,而施氮处理比不施氮处理Fv/Fm值增加了2.85%～4.18%。叶绿素荧光参数qL、ΦPSII在NPK处理中表现为齐穗期较高, 20 d之后显著降低;在50F+50M、30F+70M处理中表现为齐穗期较低, 20 d以后显著增加;在PK处理、70F+30M处理中一直处于较高水平。NPQ变化趋势与qL基本相反。ETR-PAR光响应曲线拟合结果表明, 70F+30M处理ETRmax、?和Ek值在齐穗期和20 d后值均最高。综合看来,等养分条件下配施30%有机肥具有最优的叶绿素荧光指标组合及籽粒产量;而配施超过50%有机肥由于前期热耗散增大使得用于光合作用的光能份额减少,而施用氮磷钾肥处理则由于后期的衰老使得光能利用效率下降。     

基于辐射和温度的ET0模型在吐鲁番地区的比较与修正

利用吐鲁番地区3个气象站2000—2015年逐日气象资料,以FAO-56 Penman-Monteith(FAO-56 PM)模型为标准,对6种ET_0模型(M-A模型、P-T模型、M-H模型、H-S模型、Traj模型和B-H模型)进行评价并修正,采用均方根误差(RMSE)、绝对平均误差(MAE)、平均相对误差(MRE)评价指标和Wilcoxon非参数检验法比较年、月尺度上各模型修正前后的估算精度,以筛选适用吐鲁番地区ET_0简化估算模型。结果表明:吐鲁番地区ET_0的主要影响因子是R_s(太阳辐射),其次是e_s(饱和水汽压)和R_n(作物表面净辐射);修正前,年尺度上,M-H模型的估算精度最高;月尺度上,各模型误差较大且与FAO-56 PM模型存在显著差异,适用性较差;修正后,各模型在年、月尺度上的精度均有明显提高,无显著差异,其中修正后的P-T、M-H和B-H模型估算精度最高,可作为吐鲁番地区ET_0简化估算模型。     

Soil Organic Carbon and Soil Physical Characteristics as Affected by Land Uses under Semiarid Irrigated Conditions

ABSTRACT

Rice-Wheat rotation is the dominant land use in the state of Punjab, resulting in over exploitation of ground water resources. Thus, it is necessary to evaluate other land uses that requires less water and are sustainable. The present investigation was planned with four land uses viz., fallow (FLU), rice-wheat (ALU), grasses (GLU) and pear (PLU) with respect to their effect on soil organic carbon and soil physical characteristics in surface and subsurface depth in district Ludhiana, Punjab. The soil organic carbon (SOC) content was higher by 10, 30.9 and 24.9% under rice-wheat, grasses, and pear than that under fallow. The grasses showed higher soil moisture characteristics curve (SMCC) and lower bulk density (Db) than that under rice-wheat. The larger mean weight diameter (MWD) were observed under pear, grasses and fallow than that under rice-wheat by 0.21, 0.51 and 0.41 mm, respectively. The saturated hydraulic conductivity (K_s) was higher in magnitude by 56.1, 55.4 and 28%, respectively under PLU, GLU and FLU over ALU. Of the evaluated land uses, pear and grasses proved to be more sustainable by retaining more moisture, maintaining better soil physical characteristics and SOC under semiarid irrigated conditions in the state on long term gradual response.     

昕水河流域径流变化及其对气候和人类活动的响应

开展流域尺度气候变化和人类活动对径流的影响研究是科学制定流域综合管理规划,并实现流域可持续管理的基础。以黄土高原丘陵沟壑区的典型流域——昕水河流域为研究对象,结合流域多年(1958—2015年)气象水文数据,采用M-K趋势检验法分析年降水、年潜在蒸发散和年径流量的变化趋势,运用双累积曲线法分析年径流量序列的突变年份,并根据生态水文分析法与水量平衡原理定量解析流域尺度气候变化和人类活动对年径流的影响。结果表明:1958-2015年,流域年径流量变化呈显著下降趋势(Z=-5.84,p<0.0001),而降水(Z=-0.72,p=0.31)和潜在蒸散发(Z=-0.5,p=0.88)变化趋势不显著。双累积曲线法表明径流量突变点为1974年和2000年,其中,1975—2000年间影响径流减少的主要因素是气候变化,气候变化导致径流量减少的贡献率为73.14%,土地利用方式仅为26.86%;而2001—2015年土地利用变化是影响径流减少的主要原因,其贡献率高达103.81%,可见土地利用变化已成为昕水河流域径流量变化的主要驱动因素。在今后的流域治理中,需合理制定土地利用方案,实现昕水河流域水土资源协调发展。     

农地确权对小农户信贷可得性的影响——基于双稳健估计方法的平均处理效应分析

基于我国9省小农户调查数据,运用双稳健的平均处理效应模型,研究了农地确权对小农户信贷可得性的影响及其作用机理。结果表明:农地确权可以提升小农户信贷可得性。进一步研究发现,需求方面的影响路径是:确权提高了小农户的土地安全性,增加了农业长期投资,并提高了信贷意愿。供给方面的影响路径是:确权增加了小农户的财产权并使其拥有了可流转大额财产,传递了还款可能性增加的信号;而且可将农地作为抵押品获得农地抵押贷款,增加了金融机构的贷款供给。不过,农地的信号传递作用大于抵押作用,农地抵押作用的发挥需要进行进一步的制度和组织创新。     

Measuring fish catch and consumption: Practical methods for small‐scale fisheries based on length as an alternative to weight‐based approaches

Small‐scale fisheries are recognised as making important contributions to nutrition and economic development despite a lack of accurate quantitative information on catches and consumption. While direct measurement remains the most appropriate way of collecting such data, it is impractical at large scales. Instead, household surveys based upon informant recall of fish caught and/or consumed are frequently used. However, the accuracy of weight recall by informants (even over short recall periods) has not been established. Using data from household surveys, the accuracy and precision of catch and consumption estimates derived from: (a) asking informants to recall weights of fish caught and (b) asking respondents to recall lengths of fish caught and converting to weight were tested. Length‐based methods, using visual aids to assist recall, were more accurate, precise and correctable. These methods could be useful for catch estimation, especially where fish are processed, sold or consumed shortly after capture.     

Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.     

中国农业面源污染排放的空间差异及其动态演变

以2003—2014年为研究时段,在测算农业面源污染排放强度的基础上,综合运用基尼系数和非参数估计方法,研究我国农业面源污染的空间差异及其动态演变,结果表明:1)我国农业面源污染排放总体下降,且表现出明显的空间差异,东部和中部地区的排放强度较高,西部和东北地区则相对较低。2)2003—2014年,中国农业面源污染排放强度的区域差异略微扩大,地区间差异是其总体差异的主要来源。3)核密度估计结果表明,中国农业面源污染排放总体差异表现为"下降-上升-下降"的波动变化趋势。4)马尔科夫链分析表明,中国农业面源污染在不同类型间的相互流动较为微弱,但从长期来看,存在向两极分化发展的趋势特征。