The Effects of Cropping Methods on Growth, Crop Index and Yield Response to Water of Rice （Oryza sativa L.） in Rainfed Agriculture

In this study, four combinations of crops： rice （C）, rice-maize （MCSI）, rice-cassava （MCS2） and rice-maize-cassava （MCS3） with 3 m × 3 m each plots at two field areas--Saptosari and Tanjungsari were observed. Both field areas are located in Gunungkidul district, South-Central of Java Island, with that 93% at those areas are 185 m to 500 m above sea level and high proportion of multiple cropping systems （MCS）. The aim of this study was to investigate the effect of different cropping method on growth, crop index and yield response to water of rice in rainfed agriculture. Mathematical models were developed to describe rice growth. The rice height was followed monomolecular function and the number of tillers followed exponential polynomial function. Crop index was calculated from climate data during plant growth phase. And yield response to water was calculated from actual evapotranspiration （ETa） and the maximum evapotranspiration （ETm）. The results showed that the height of rice was not significantly different between each combination （P 〉 0.05）. Number of tillers was also not significant （P 〉 0.05）. However, monoculture treatment had more number of tillers than rice in MCS. Crop index of rice at Saptosari was higher than at Tanjungsari. Based on the calculation of evapotranspiration （ET）, water deficit at initial was less than at mid-season （ETa 〈 ETm） and affected water stress. Statistical analysis showed that cropping methods did not significantly affect rice growth and yield （P 〉 0.05）.

The influences of canopy temperature measuring on the derived crop water stress index

Crop water stress index(CWSI)is widely used for efficient irrigation management.Precise canopy temperature(T_(c))measurement is necessary to derive a reliable CWSI.The objective of this research was to investigate the influences of atmospheric conditions,settled height,view angle of infrared thermography,and investigating time of temperature measuring on the performance of the CWSI.Three irrigation treatments were used to create different soil water conditions during the 2020-2021 and 2021-2022 winter wheat-growing seasons.The CWSI was calculated using the CWSI-E(an empirical approach)and CWSI-T(a theoretical approach)based on the T_(c).Weather conditions were recorded continuously throughout the experimental period.The results showed that atmospheric conditions influenced the estimation of the CWSI;when the vapor pressure deficit(VPD)was>2000 Pa,the estimated CWSI was related to soil water conditions.The height of the installed infrared thermograph influenced the T_(c)values,and the differences among the T_(c)values measured at height of 3,5,and 10 m was smaller in the afternoon than in the morning.However,the lens of the thermometer facing south recorded a higher T_(c)than those facing east or north,especially at a low height,indicating that the direction of the thermometer had a significant influence on T_(c).There was a large variation in CWSI derived at different times of the day,and the midday measurements(12:00-15:00)were the most reliable for estimating CWSI.Negative linear relationships were found between the transpiration rate and CWSI-E(R^(2)of 0.3646-0.5725)and CWSI-T(R^(2)of 0.5407-0.7213).The relations between fraction of available soil water(FASW)with CWSI-T was higher than that with CWSI-E,indicating CWSI-T was more accurate for predicting crop water status.In addition,The R^(2)between CWSI-T and FASW at 14:00 was higher than that at other times,indicating that 14:00 was the optimal time for using the CWSI for crop water status monitoring.Relative higher yield of winter wheat was obtained with average seasonal values of CWSI-E and CWSI-T around 0.23 and 0.25-0.26,respectively.The CWSI-E values were more easily influenced by meteorological factors and the timing of the measurements,and using the theoretical approach to derive the CWSI was recommended for precise irrigation water management.

Monitoring the Sequential Cropping Index of Arable Land in Zhejiang Province of China Using MODIS-NDVI

The sequential cropping index of arable land is important agricultural information. The aim of this article is to monitor and analyze the parameter, and offer reference for agricultural production. The cropping index of arable land in Zhejiang Province, China from 2001 to 2004 was calculated using the second order difference based MODIS （moderate resolution imagine spectroradimeter） vegetation data from NASA （National Aeronautic and Space Administration） in America and the land use map with a scale of 1：25 000. It was found that the peak of the time series of the NDVI curve indicated that the ground biomass of crops reached the maximum, and fluctuated with the crops growing processes such as sowing, seeding, heading, ripeness, and harvesting within one year. Thus, the sequential cropping index was defined as the number of peaks of the time series of the NDVI curve. The sequential cropping index of all cities in Zhejiang Province, China was worked out. It is seen from the spatial distribution that the cropping index in the southwest Zhejiang Province is larger than that in the northeast. As for the temporal distribution, the sequential cropping index decreased from 2001 to 2003, whereas it increased slightly from 2003 to 2004. However, the index of arable land was relatively low, as far as the geographic position and climatic resource were concerned, and the potential of the sequential cropping index was great.

The concept and statistical method of drought resistance index in crops

There is evindence showing that stress susceptibility index(SSI)(1一Yd/Yp)/(1—(?)d/(?)p)used as a measure of drought resistance of crop on the field is an altered form of droughtresistance coefficient(DRC)(Yd/Yp).The correlative coefficient SSI and DRC is r=-1.Therefore,the SSI doesn’t improve the defect of the DRC.After two years experiments per-formed by using thirty winter wheat varieties as trial materials,the concept of drought resistanceindex in crops was put forward.Its expressing equation is:the yield in drylan×drought resis-tance coefficient/average yield in dryland.It makes the drought resistance coefficient(physicalindex)correlate well with the yield in dryland(agronomy index)and is suitable for breeder.

Introducing a drought index to a crop model can help to reduce the gap between the simulated and statistical yield

A well-established and pre-calibrated crop model can normally represent the overall characteristics of crop growth and yield.However,it can hardly include all relevant factors that affect the yield,and usually overestimates the crop yield when extreme weather conditions occur.In this study,the authors first introduced a drought index(the Standardized Precipitation Evapotranspiration Index)into a process-based crop model(the Agro-C model).Then,the authors evaluated the model’s performance in simulating the historical crop yields in a double cropping system in the Huang-Huai-Hai Plain of China,by comparing the model simulations to the statistical records.The results showed that the adjusted Agro-C model significantly improved its performance in simulating the yields of both maize and wheat as affected by drought events,compared with its original version.It can be concluded that incorporating a drought index into a crop model is feasible and can facilitate closing the gap between simulated and statistical yields.

Using Crop Management Scenario Simulations to Evaluate the Sensitivity of the Ohio Phosphorus Risk Index

Phosphorus (P) risk indices are commonly used in the USA to estimate the field-scale risk of agricultural P runoff. Because the Ohio P Risk Index is increasingly being used to judge farmer performance, it is important to evaluate weighting/scoring of all P Index parameters to ensure Ohio farmers are credited for practices that reduce P runoff risk and not unduly penalized for things not demonstrably related to runoff risk. A sensitivity analysis provides information as to how sensitive the P Index score is to changes in inputs. The objectives were to determine 1) which inputs are most highly associated with P Index scores and 2) the relative impact of each input variable on resultant P Index scores. The current approach uses simulations across 6134 Ohio point locations and five crop management scenarios (CMSs), representing increasing soil disturbance. The CMSs range from all no-till, which is being promoted in Ohio, rotational tillage, which is a common practice in Ohio to full tillage to represent an extreme practice. Results showed that P Index scores were best explained by soil test P (31.9%) followed by connectivity to water (29.7%), soil erosion (13.4%), fertilizer application amount (11.3%), runoff class (9.5%), fertilizer application method (2.2%), and finally filter strip (2.0%). Ohio P Index simulations across CMSs one through five showed that >40% scored <15 points (low) while <1.5% scored >45 points (very high). Given Ohio water quality problems, the Ohio P Index needs to be stricter. The current approach is useful for Ohio P Index evaluations and revision decisions by spatially illustrating the impact of potential changes regionally and state-wide.

气候变化背景下山西省气象干旱时空演变特征

干旱频发对生态资源、农业发展造成了严重影响,为揭示山西省干旱时空演变特征,基于1971—2020年山西省24个气象站点的逐月气象资料,利用改进的Mann-Kendall方法检验各气象因子的年变化趋势,采用FAO56 Penman-Monteith公式计算参考作物腾发量(ET0),分析单个气象因子变化情况下ET0的变化特征和对气象因子的敏感性,比较各时间尺度(月、季、年尺度)不同干旱指数(降水距平百分率(Pa)、标准化降水指数(SPI)和标准化降水蒸散指数(SPEI))对山西省干旱灾害监测能力。结果表明:ET0与相对湿度呈负相关,气象因子对ET0的敏感性由大到小依次为相对湿度、日最高气温、2 m处风速、日最低气温、日平均气温,ET0呈波动下降趋势。SPEI能够在多时间尺度上有效反映山西省干旱状况,是该地区干旱监测的有效工具。在月、季、年尺度下,比较3个干旱指数,Pa检测效果较差,SPI和SPEI在某些地理区域存在较大差异,整体而言,SPEI在多数地区检测干旱的性能更好;SPEI-1尺度下,各干旱等级发生频率由大到小依次为轻旱(14.8%)、中旱(10.6%)、重旱(5.6%)、特旱(1.9%),3月干旱发生率最高(34%),12月发生率最低(31.8%),吕梁市、晋中市、大同市干旱情况较为严重;SPEI-3尺度下,季节发生干旱频率由大到小依次为秋季(33.5%)、夏季(32.5%)、春季(31.9%)、冬季(31.4%),大同市、长治市特旱发生频率最高,旱情最为严重,忻州市轻旱频率、朔州市中旱频率、吕梁市重旱频率最高;SPEI-12尺度下,轻、中、重、特旱频率分别为14.8%、10.5%、5.4%、2.3%,SPEI-12相较SPEI-1和SPEI-3识别重旱、特旱的站点更多,并基于游程理论得出,山西省南部干旱频次更多,东部干旱历时更长、干旱严重程度更大,干旱峰值主要出现在山西省南北部,由于年均降水呈波动性下降,年均气温整体上升,山西省的气候趋于暖干化,南北部旱情将有所加重,中部地区旱情有所减缓,全域性干旱仍有很大发生可能。

Heavy Metal Characteristics and Comprehensive Quality Index Evaluation of Soil-Crop System in 11 Cities of Yunnan Province, China

Yunnan province in China is a high background area of soil heavy metals, and agricultural planting and industrial and mining activities are relatively frequent, which aggravate soil heavy metal pollution. However, at present, there are few reports on the overall or large-scale soil-crop pollution and risk assessment of heavy metals in Yunnan Province. This study through 11 cities in Yunnan province of China farmland soil-crop systems of heavy metal lead, cadmium content, enrichment coefficient is analyzed, and using the method of potential ecological harm index, index of compressive quality to evaluate heavy metal pollution soil-crop system risk. Results showed that the average content of soil heavy metal Cd and Pb were 1.31 mg/kg, 64.17 mg/kg, which are higher than the background value of Yunnan province. The average contents of Pb and Cd in the edible parts of crops were 0.20 mg/kg, 0.08 mg/kg. The average content of heavy metals in crops in Diqing (Pb) and Nujiang (Cd) was 0.72 mg/kg and 0.148 mg/kg. The enrichment coefficients of heavy metals in edible parts of crops were the largest in Diqing (Pb) and Zhaotong (Cd). The average value of ecological risk index of Pb element in soil is 2.79, which indicates that the study area is in a slight ecological hazard, the average value of the ecological risk index of Cd in soil is 126.43. The average value of the comprehensive quality impact index (IICQ) is 4.27, which indicates that the study area is moderately polluted. In this study, the contents of heavy metals Cd and Pb in soils and crops in different administrative regions were determined, and the heavy metals Pb and Cd in soil-crop system of Yunnan province, China were evaluated, it is expected to have important scientific and theoretical significance for the safe use of cultivated land to export safe agricultural products and promote the sustainable development of agriculture in Yunnan Plateau.

长岭县近60年干旱特征分析

本文使用1961—2020年逐日基础气象资料,采用FAO Penman-Monteith计算参考作物蒸散量ET0,根据农业干旱等级国标计算作物水分亏缺指数CWDI及作物水分亏缺距平指数CWDI_(a),根据作物水分亏缺距平指数CWDI_(a)干旱等级标准及玉米干旱灾害风险评价方法计算玉米生产5~9月逐旬干旱、轻旱、中旱、重旱、特旱发生频率,对5~9月干旱发生规律及干旱年代际变化规律进行分析。结果表明,作物生长季5~9月轻旱、中旱、重旱发生频率多在10%以下,出现频率较低,特旱发生频率最高。特旱及干旱在6月上中旬、9月上中旬出现两个峰值,6月下旬至8月下旬干旱发生频率作物生长季最低。2001—2010年轻旱、中旱、重旱、特旱及干旱发生频率最高,2011—2020年干旱发生频率最低。9月轻旱有随年代际增加趋势,7月中旬至8月中旬中旱有随年代际增加趋势,重旱各年代5月上中旬、8月下旬至9月中旬维持较高发生频率。特旱各年代都维持较高发生频率,7月中旬至8月下旬特旱有随年代际增加趋势。

东北地区未来春玉米干旱风险时空分布及对气候变化的响应

【目的】干旱是影响中国农业生产的主要自然灾害之一。东北地区作为中国最大的玉米生产基地,气候变化背景下干旱频发重发严重影响玉米的高产稳产。评估未来气候情景下东北地区春玉米干旱发生风险及其空间格局变化,为该地区春玉米防旱避灾以及保障其高产稳产提供科学依据。【方法】选取东北地区春玉米潜在种植区为研究区域,基于ISIMIP输出的SSP1-2.6、SSP3-7.0和SSP5-8.53种气候情景的1981—2060年逐日气象资料以及53个农业气象观测站1981—2014年春玉米生育期资料,选取作物水分亏缺指数(crop water deficit index,CWDI)为农业干旱指标,分析东北地区春玉米不同生育时期不同等级干旱时空分布特征,选择最优概率理论分布函数进行干旱指数序列的概率估算,基于信息扩散理论估算得到各点春玉米不同等级干旱风险,构建干旱风险指数,评估未来不同气候情景下东北地区春玉米干旱发生风险及未来各等级风险区的空间格局变化。【结果】(1)1981—2014年东北地区春玉米全生育期干旱指数总体呈西南高东北低的特征,表现为内蒙古东四盟(57.3%)>黑龙江省(40.6%)>辽宁省(39.5%)>吉林省(38.9%)。(2)研究区域春玉米生育中期干旱指数整体高于生育前期和生育后期。其中,2030s和2050s研究区域春玉米生育前期干旱风险概率为轻旱>中旱≈重旱>特旱,生育中期干旱风险概率为特旱>重旱>轻旱≈中旱,生育后期干旱风险概率轻旱>中旱>重旱>特旱。(3)1981—2060年,SSP1-2.6低排放情景下,东北地区春玉米较高等级干旱风险发生概率将减少,极高和较高干旱风险区明显向西南收缩,2030s和2050s面积占比分别减少5.4%和9.6%、0.8%和2.5%;而SSP3-7.0和SSP5-8.5两个高排放情景下,较高等级干旱风险发生概率增加,且较高干旱风险区向东北扩张,2050s面积占比分别增加8.5%和9.7%。【结论】基于干旱风险指数的未来干旱风险时空分布格局中,东北春玉米干旱风险呈现由西南向东北减少的特征,且未来SSP3-7.0和SSP5-8.5情景下,较高干旱风险区向东北方向扩张,需关注作物关键生育时期提出针对性的防御措施。

种植结构“趋粮化”:劳动力外流的影响及其异质性

已有关于农业种植结构“趋粮化”研究忽视粮食作物的品种结构差异和区域差异,存在以户为单位度量劳动力外流的选择差错及要素替代发生主体不一致带来的行为逻辑分析偏误。基于全国农村固定观察点2004—2018年村级面板数据,实证分析劳动力外流对农业种植结构“趋粮化”的影响及其异质性。结果表明:相悖于现有文献的主流观点,整体上农村劳动力外流对粮食种植比例的影响不显著,水稻主产区农村劳动力外流甚至会显著抑制种植结构“趋粮化”,这一结论经过多重稳健性检验依然成立。原因在于,劳动力外流显著促进了土地流转和规模经营,强化了水稻种植“双改单”和复种指数下降的趋势。

基于CWDI蒙冀半干旱区近60a谷子干旱时空变化特征

谷子是半干旱区的特色作物,具有耐旱稳产的特点。蒙冀半干旱区是谷子的优势产区,种植面积及总产量均居全国前列,但干旱严重威胁着该区谷子生产。为揭示干旱对谷子生长过程的影响,提高谷子生产应对干旱风险的能力,选取研究区内27个气象站点1961-2019年谷子生长季(5-9月)逐日气象数据,采用作物水分亏缺指数(CWDI)作为干旱指标,结合干旱强度、干旱频率、干旱站次比分析谷子干旱的时空变化特征。结果表明:(1)近60a,蒙冀半干旱区谷子干旱强度呈下降趋势。干旱的发生频率和站次比均随干旱等级的升高而降低。(2)在谷子各生育阶段,干旱强度和干旱频率表现为拔节期>苗期>成熟期>灌浆期>抽穗期。(3)空间分布上,研究区内谷子干旱强度和干旱频率均表现为由西北向东南逐渐降低,其中中旱、重旱和特旱的发生范围均减小。干旱强度和频率的降低有利于蒙冀半干旱区的谷子生产。

2001-2020年黄土高原植被生长季干旱的时空分布

基于MODIS实际蒸散量(ET)和潜在蒸散量(PET)反演作物缺水指数(CWSI),研究黄土高原生长季干旱时空变化特征,并用CWSI和植被归一化指数(NDVI)进行偏相关分析以探究生长季内最干旱月份对NDVI的影响。结果表明:1)黄土高原生长季CWSI多年平均值为0.777,处于中度干旱状态,空间分布表现为东南部较轻,西北部较严重的特征,且高程在1200~1700 m区域内的干旱程度最高;2)2001-2020年多年生长季CWSI整体呈显著下降趋势,其中2001年干旱程度最高,2018年干旱程度最低;在整个生长季内,黄土高原在4月的干旱程度最高;8月的干旱程度最低。具体表现为:4-10月,干旱等级从重旱过渡到中旱和轻旱,又从轻旱演变成中旱;3)从植被类型角度分析,荒漠、草原的分布区域属于重旱;草甸、栽培植被、灌丛的分布区域属于中旱;针叶林、阔叶林的分布区域属于轻旱,且不同植被类型在植被生长季的CWSI均呈波动下降趋势。研究结果可为黄土高原地区的生态预警和旱灾预估提供科学依据。

基于闪烁通量和土壤水分的春玉米干旱研究

为了识别河北省怀来县春玉米生长季内不同生育期的干旱情况。基于大孔径闪烁仪观测系统的数据,本研究用河北省怀来县2021年春玉米的实际蒸散量代替潜在蒸散量,计算了修订的水分亏缺指数(RCWDI)。根据土壤相对湿度得到的春玉米实际干旱等级,重新构建了RCWDI的干旱等级,并利用2019年的数据进行了验证。结果表明:2021年怀来县春玉米生育期内降水量和实际蒸散量之间有显著的相关关系,降水量与蒸散量差值的累积值可以反映春玉米的干旱状况。2021年怀来县春玉米干旱主要发生在播种到拔节期。2019年利用RCWDI获得的干旱等级与基于土壤水分得到的干旱等级相似度高达80%。研究表明基于闪烁通量修订的水分亏缺指数及其干旱等级具有一定的适用性。

马铃薯水分胁迫指数模型优化研究

【目的】探究马铃薯的叶气温差与环境因子的关系,进一步优化马铃薯水分胁迫指数模型。【方法】在河南农业大学林学院试验基地进行马铃薯盆栽试验,选择晴朗天气测定不同土壤含水率下马铃薯的叶气温差随太阳辐射和大气饱和水汽压差(VPD)的变化规律,确定作物水分胁迫指数(crop water stress index,CWSI)的上下基线,进一步试验后得到优化后的马铃薯CWSI经验模型,并对相关模型进行验证。【结果】马铃薯的叶气温差随着土壤含水率的降低而升高;当土壤含水率较低(7.28%)时,马铃薯的叶气温差随太阳辐射的增大而增大,呈显著线性关系;当土壤含水率较高(15.85%)时,马铃薯的叶气温差随VPD的增大而减小,呈显著线性关系;构建出马铃薯CWSI的上基线为y=0.0098Q-0.68[Q为太阳辐射强度/(W·m^(-2))],下基线为y=-1.67V+3.75(V为大气饱和水汽压差/kPa);将优化的CWSI模型验证后得知,随着土壤含水率的减少,CWSI值增加,且CWSI同土壤含水量呈极显著负相关关系(p<0.01)。【结论】马铃薯的最大叶气温差与太阳辐射的线性关系作为马铃薯水分胁迫指数的上基线是可行的,该研究对传统CWSI经验模型进行改进,进一步优化了CWSI经验模型。

“油-稻-稻”三熟制早熟冬油菜氮高效基因型及鉴定指标筛选

【目的】旨在筛选适宜“油-稻-稻”三熟制的氮高效早熟冬油菜基因型及其简易筛选指标。【方法】利用田间小区试验,选用了生育期185 d以下的的适宜“油-稻-稻”三熟制种植的14种早熟冬油菜基因型,进行正常施氮(N_(1))和不施氮(N_(0))处理。根据不同冬油菜基因型氮肥利用率(NUE)的差异,利用树状聚类分析进行分类,形成高、中、低3种氮效率基因型。同时通过逆向溯源的方法,观察分析3种氮效率基因型在不同氮水平条件下的产量构成、主要农艺性状、干物质积累及氮素吸收等方面的表现特征。总结分析出氮高效冬油菜基因型的一些简易判断的指标。【结果】不同氮效率基因型冬油菜在产量形成、主要农艺性状等方面存在显著差异。N1处理下成熟期早熟冬油菜的主要农艺性状的表型值和变异系数基本表现大于N0处理。因此,以N1处理下不同基因型的农艺性状表现作为不同氮效率基因型的鉴别标准。N1处理下,氮高效基因型比氮中效和氮低效基因型产量高、单株角果数多、每角粒数多、株高高、主花序长、一级分枝数多、角果密度高、各部位干物质积累量高、籽粒中氮含量高、氮肥偏生产力、氮肥农学效率、氮肥生理利用率和氮收获指数均较高。【结论】沣油737是适合南方“油-稻-稻”三熟制的氮高效早熟冬油菜;正常施氮条件下,成熟期较多的单株角果数与每角粒数,以及较高株高是判断早熟冬油菜氮高效基因型的简易指标。

河南省夏玉米不同生长阶段干旱风险分析

【目的】研究夏玉米各生长阶段的抗干旱风险,为预防夏玉米受干旱影响导致产量和品质下降,降低干旱造成的损失提供理论依据。【方法】本研究利用河南省16个气象站1981—2019年的逐日气象数据,以作物水分亏缺指数(CWDI)为评判指标,根据《北方夏玉米干旱等级》(QX/T 260—2015)对该地区玉米5个生长发育时期进行了干旱等级划分,分析各等级干旱发生的频率、干旱风险指数的时空分布规律。【结果】河南省夏玉米各生长阶段干旱发生总频率在豫北地区最高,其次是豫中和豫东。干旱发生总频率均在夏玉米抽雄—乳熟期最大,高达96%以上,且以中旱和特旱为主。不同等级干旱发生的频率具有差异性,但豫南地区各生长阶段各等级干旱发生的频率相对较低。在夏玉米播种—出苗、出苗—拔节期,河南省各地区干旱风险指数呈由南向北逐渐增加的空间分布特征;在拔节—抽雄、抽雄—乳熟和乳熟—成熟期,该指数基本在30以上。【结论】各地区夏玉米在拔节—抽雄、抽雄—乳熟和乳熟—成熟期干旱发生的风险均较高。夏玉米各个生长阶段干旱发生的风险均为豫北地区最高,豫南地区最低。应重点关注豫北地区的防旱减灾工作。

基于CROPWAT模型的玉米水分盈亏及灌溉制度研究

为研究黑龙江省中部地区玉米生育期内需水量、水分盈亏指数(CWSDI)、干旱等级变化和不同水文年灌溉制度,基于哈尔滨市1955-2014年气象数据、土壤数据和玉米作物参数,利用CROPWAT模型计算玉米生育期内需水量、有效降雨量和灌溉需水量,计算水分盈亏指数并划分干旱等级,采用Mann-Kendall趋势检验分析以上因素变化趋势,针对不同典型水文年制定灌溉制度。结果表明,该地区1955-2014年生育期内玉米需水量以9.41 mm/10 a的速率下降;丰水年、平水年,枯水年和特枯水年需水量分别为407.80、423.80、452.00和485.60 mm;多年平均CWSDI没有明显变化,生育期内每月CWSDI变化较为明显,干旱等级分析表明在玉米生长初期和生长后期较干旱;不同水文年干旱情况不同,除丰水年外,有效降雨量均难以满足玉米生育期内需水量,不同典型水文年应建立不同的灌溉制度。特枯水年、枯水年和平水年的灌水净定额分别为151.30、117.10和39.70 mm。

不同抚育间伐方式对平原生态林空间结构的影响

为探讨不同抚育间伐方式对北京市平原生态林林分空间结构的影响,在北京市通州区选取旱柳、国槐和白蜡等代表性林分布设9个固定样地。在选取目标树后,每个林分类型分别采取干扰木采伐、群团状采伐以及带状间伐,采用优化后的大小比数、角尺度和密集度3个常用的林分空间结构指标,分析平原生态林空间结构现状以及间伐对目标树及林分空间结构的影响。利用3个指标构建空间结构综合指数(CSSI)来综合评价不同间伐方式对北京市平原生态林空间结构的影响。结果表明:(1)CSSI结合了角尺度、密集度和优化后的大小比数,适用于北京平原生态林的空间结构评价。(2)以国槐、白蜡和旱柳为代表的平原生态林间伐前空间分布格局呈均匀分布,大部分林木呈中庸状态,林分比较密集,白蜡和旱柳的空间结构优于国槐。(3)抚育间伐能显著优化平原生态林空间结构,尤其对于国槐等保留密度较高的林分优化作用明显。(4)对于不同树种来说,改善空间结构的最优间伐方式不尽相同,但对于目标树空间结构单元来说,针对目标树的干扰树采伐优化程度最为明显。

基于Sentinel-2遥感影像的莓茶空间分布研究

本研究以M地为研究区,基于多时相Sentinel-2影像数据,采用典型植被指数NDVI和EVI时间序列变化特征构建决策树分类模型,提取研究区莓茶种植区域并绘制莓茶种植区域图;利用决策树分类方法、最大似然法和支持向量机进行分类精度对比。结果表明,(1)M地莓茶种植主要分布于中部与北部的T地、M地和G地,东部与南部的D地和Q地分布较少;(2)通过3种分类结果对比发现,决策树分类方法(总体精度为97.2%,Kappa系数为0.963)最优,其次为支持向量机(总体精度为92.9%,Kappa系数为0.896),最大似然法分类效果(总体精度为91.7%,Kappa系数为0.888)最差。该研究可为莓茶种植范围提取在数据源与时序特征构建方面提供参考。