Effect of Ploughing Depth on the Growth and Yield of Heracleum candicans： a Threatened Medicinal Herb and a Less-explored Potential Crop of the Himalayan Region     

Hemant K. Badola Jitendra S. Butola 《山地科学学报》2005,2(2):173-180

This paper examines the effect of ploughing depths （A -- 60 cm, B -- 45 cm and C -- 30 cm） on the growth and yield of Heracleum candicans Wall （Apiaceae）, a threatened medicinal herb of the Himalayan region. This less-explored plant is being suggested as a potential crop for the mountain agriculture. The study was carried out in an orchard in Himachal Pradesh, India at 2500 m altitude, for two successive growth years. During the first year, all plants remained in juvenile state; in the second year, nearly 65 % plants produced flowers only under 60cm ploughing depth. Among its morphological traits, plant height, collar diameter and aboveground flesh weight were found to be strongly correlated （P 〈 0.01） with the belowground biomass during the first year （r =0.968, 0.925 and 0.973, respectively） and during the second year （r=0.945, 0.928 and 0.775, respectively）. Increase in the ploughing depth was significantly correlated （P〈0.01） with all growth parameters, including the belowground dry weight, marketable portion of the produce. The belowground biomass （commercial yield; 16.28 Qt/hec） at depth A was about 2.6 and 4.7 times higher than those recorded at depths B and C, respectively. The results clearly justify the importance of deep ploughing and this paper strongly recommends it for economically sustainable cropping.  相似文献   

The use of MODIS data to derive acreage estimations for larger fields: A case study in the south-western Rostov region of Russia     

S. Fritz  M. Massart  I. Savin  J. Gallego  F. Rembold   《International Journal of Applied Earth Observation and Geoinformation》2008,10(4):453

Recent developments in remote sensing technology, in particular improved spatial and temporal resolution, open new possibilities for estimating crop acreage over larger areas. Remotely sensed data allow in some cases the estimation of crop acreage statistics independently of sub-national survey statistics, which are sometimes biased and incomplete. This work focuses on the use of MODIS data acquired in 2001/2002 over the Rostov Oblast in Russia, by the Azov Sea. The region is characterised by large agricultural fields of around 75 ha on average. This paper presents a methodology to estimate crop acreage using the MODIS 16-day composite NDVI product. Particular emphasis is placed on a good quality crop mask and a good quality validation dataset. In order to have a second dataset which can be used for cross-checking the MODIS classification a Landsat ETM time series for four different dates in the season of 2002 was acquired and classified. We attempted to distinguish five different crop types and achieved satisfactory and good results for winter crops. Three hundred and sixty fields were identified to be suitable for the training and validation of the MODIS classification using a maximum likelihood classification. A novel method based on a pure pixel field sampling is introduced. This novel method is compared with the traditional hard classification of mixed pixels and was found to be superior.  相似文献   

基于遥感信息的华北冬小麦区域生长模型及模拟研究   总被引：21，自引：1，他引：21  

马玉平  王石立  张黎  侯英雨  庄立伟  王馥棠 《气象学报》2005,63(2):204-215

卫星遥感估产和作物生长模拟在作物监测和产量预测方面有各自不可替代的优势。但是,遥感估产难以揭示作物生长发育和产量形成的内在机理,作物模拟在区域应用时初始值的获取和参数的区域化遇到很多困难。如何利用二者的互补性使其相互结合受到人们关注。该文在Wofost模型本地化和区域化的基础上,首次利用同化法的思路探讨了MODIS遥感信息与华北冬小麦生长模拟模型结合的可行性和方法,初步建立了潜在生产水平(水分适宜条件)下区域遥感-作物模拟框架模型(WSPFRS模型)。模拟结果显示:WSPFRS模型对区域尺度的出苗期重新初始化后,模拟的开花期、成熟期空间分布的准确性比Wofost模拟结果有所改进;利用遥感信息对区域尺度上返青期生物量重新初始化后,模拟贮存器官干重的空间分布更接近实际单产的分布,贮存器官干重的高值区与实际高产区基本相符。该研究将为下一步实际水分供应条件下基于遥感信息的冬小麦区域生长模拟研究奠定了基础。  相似文献   

Detection of rice sheath blight for in-season disease management using multispectral remote sensing     

《International Journal of Applied Earth Observation and Geoinformation》2005,7(2):115-128

Timely diagnosis of crop diseases in fields is critical for precision on-farm disease management. Remote sensing technology can be used as an effective and inexpensive method to identify diseased plants in a field scale. However, due to the diversity of crops and their associated diseases, application of the technology to agriculture is still in research stage, which needs to be elaborately investigated for algorithm development and standard image processing procedures. In this paper, we examined the applicability of broadband high spatial-resolution ADAR (Airborne Data Acquisition and Registration) remote sensing data to detect rice sheath blight and developed an approach to further explore the applicability. Based on the field symptom measurements, a comprehensive field disease index (DI) was constructed to measure infection severity of the disease and to relate to image sampled infections. In addition to direct band digital number (DN) values, band ratio indices and standard difference indices were used to examine possible correlations between field and image data. The results indicated that the broadband remote sensing imagery has the capability to detect the disease. Some image indices such as RI₁₄, SDI₁₄ and SDI₂₄ worked better than others. A correlation coefficient above 0.62 indicated that these indices would be valuable to use for identification of the rice disease. In the validation analysis, we obtained a small root mean square error (RMS = 9.1), confirming the applicability of the developed method. Although the results were encouraging, it was difficult to discriminate healthy plants from light infection ones when DI < 20 because of their spectral similarities. Hence, it was clear that identification accuracy increases when infection reaches medium-to-severe levels (DI > 35). This phenomenon illustrated that remote sensing images with higher spectral resolution (more bands and narrower bandwidth) were required in order to further examine the capability of separating the light diseased plants from healthy plants.  相似文献   

干旱半干旱地区农田土壤NO3-N深层积累及其影响因素   总被引：7，自引：0，他引：7  

郭胜利  张文菊  党廷辉  吴金水  郝明德 《地球科学进展》2003,18(4):584-591

以长期试验资料为基础,着重分析了干旱半干旱地区农田系统中施肥、作物、降水、耕作措施以及土壤类型和特性对产生土壤NO3-N深层积累的影响.分析发现,氮肥的过量施用和400～800 mm降水量偏低是导致干旱半干旱地区土壤NO3-N积累在100～300 cm土层的主要因素.随着氮肥用量的增加,NO3-N深层积累显著增加;氮磷配施有助于降低其积累量.不同作物对氮素的吸收利用效率也是影响NO3-N深层积累的因素,作物之间的轮作方式会有效降低NO3-N深层积累;休闲期种植合理植物可有效降低NO3-N深层积累.NO3-N深层积累主要产生在质地较重的土壤上,带正电荷粘土矿物对NO3-N吸附是导致热带土壤中NO3-N积累的主要因素.深入研究深层积累NO3-N的生物有效性、迁移变化机理、与作物根系之间的关系以及对土壤性状和环境的影响具有重要意义.  相似文献   

Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data     

《International Journal of Applied Earth Observation and Geoinformation》2016

A sufficient number of satellite acquisitions in a growing season are essential for deriving agronomic indicators, such as green leaf area index (GLAI), to be assimilated into crop models for crop productivity estimation. However, for most high resolution orbital optical satellites, it is often difficult to obtain images frequently due to their long revisit cycles and unfavorable weather conditions. Data fusion algorithms, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), have been developed to generate synthetic data with high spatial and temporal resolution to address this issue. In this study, we evaluated the approach of assimilating GLAI into the Simple Algorithm for Yield Estimation model (SAFY) for winter wheat biomass estimation. GLAI was estimated using the two-band Enhanced Vegetation Index (EVI2) derived from data acquired by the Operational Land Imager (OLI) onboard the Landsat-8 and a fusion dataset generated by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) data and the OLI data using the STARFM and ESTARFM models. The fusion dataset had the temporal resolution of the MODIS data and the spatial resolution of the OLI data. Key parameters of the SAFY model were optimised through assimilation of the estimated GLAI into the crop model using the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm. A good agreement was achieved between the estimated and field measured biomass by assimilating the GLAI derived from the OLI data (GLAI_L) alone (R² = 0.77 and RMSE = 231 g m⁻²). Assimilation of GLAI derived from the fusion dataset (GLAI_F) resulted in a R² of 0.71 and RMSE of 193 g m⁻² while assimilating the combination of GLAI_L and GLAI_F led to further improvements (R² = 0.76 and RMSE = 176 g m⁻²). Our results demonstrated the potential of using the fusion algorithms to improve crop growth monitoring and crop productivity estimation when the number of high resolution remote sensing data acquisitions is limited.  相似文献   

农业生态的化学元素分类及其与作物优质高产的关系   总被引：12，自引：0，他引：12  

曾昭华 《湖南地质》2001,20(4):277-283

利用国家科学技术成果，并根据国内外研究现状，将103个化学元素分成对作物生长发育、增产起显著作用、起一定作用、起有害作用和起未知作用4个农业生态元素类型，进而论述了它们与作物优质高产的关系。  相似文献   

广东近40年来5～9用气候变化及与主要农作物关系     

刘黎明  陈创买 《热带地理》1996,(2)

本文以广东省４７个测站近４０年来６～９月降水量、平均气温、总日照时数和一些主要农作物逐年单产量资料为基础，用主分量分析、周期回归等方法，分析广东全省性的气候时空变化特征；分析一些主要农作物逐年单产的变化规律．用多元逐步回归分析方法探讨了广东全省性气候变化与同期及后期（１～３年）主要农作物单产量间的关系并作了预报．  相似文献   

Analyzing multi-year rice-fallow dynamics in Odisha using multi-temporal Landsat-8 OLI and Sentinel-1 Data     

Parvesh Kumar Chandna  Saptarshi Mondal 《地理信息系统科学与遥感》2020,57(4):431-449

ABSTRACT

Sustainable intensification of existing cropland is one of the most viable options for meeting the escalating food demands of the ever-increasing population in the world. Accurate geospatial data about the potential single-crop (rice-fallows) areas is vital for policymakers to target the agro-technologies for enhancing crop productivity and intensification. Therefore, the study aimed to evaluate and understand the dynamics of rice-fallows in the Odisha state of India, using SAR (Sentinel-1) and Optical (Landsat OLI) datasets. This study utilized a decision-tree approach and Principal component analysis (PCA) for the segmentation and separation of different vegetation classes. The estimated overall accuracy of extracted rice-fallow maps was in the range of 84 to 85 percent. The study identified about 2.2, 2.0 and 2.1mha of Rice-Fallows in the years 2015–16, 2016–17, and 2017–18, respectively. The combined analysis (all three years) of rice-fallow maps identified about 1.34mha of permanent rice-fallows, whereas the remaining 0.6–0.8mha area was under the current-fallow category. About 50% of the total permanent rice-fallows were detected in the rainfed areas of Mayurbhanj, Bhadrak, Bolangir, Sundargarh, Keonjhar, Baleswar, Nawarangpur and Bargarh districts. The study also illustrated the time-series profiles of SMAP (soil moisture) datasets for the ten agroclimatic zones of the Odisha, which can be utilized (along with rice-fallow maps) for the selection of crop and cultivars (e.g. short or medium duration pulses or oilseeds) to target the rice fallows. The approach utilized in the current study can be scaled up in similar areas of South and South-east Asia and Africa to identify single-crop areas for targeting improved technologies for enhanced crop productivity and intensification.  相似文献   

Multi-scale remote sensing to support insurance policies in agriculture: from mid-term to instantaneous deductions     

Sarvia F  De Petris S  Borgogno-Mondino E 《地理信息系统科学与遥感》2020,57(6):770-784

ABSTRACT

Climate change is today one of the biggest issues for farmers. The increasing number of natural disasters and change of seasonal trends is making insurance companies more interested in new technologies that can somehow support them in quantifying and mapping risks. Remotely sensed data, with special focus on free ones, can certainly provide the most of information they need, making possible to better calibrate insurance fees in space and time. In this work, a prototype of service based on free remotely sensed data is proposed with the aim of supporting insurance companies’ strategies. The service is thought to calibrate annual insurance rates, longing for their reduction at such level that new customers could be attracted. The study moves from the entire Piemonte region (NW Italy), to specifically focus onto the Cuneo province (Southern Piemonte), which is mainly devoted to agriculture. MODIS MOD13Q1-v6 and Sentinel-2 L2A image time series were jointly used. NDVI maps from MODIS data were useful to describe the midterm phenological trends of main crops at regional level in the period 2000–2018; differently, Sentinel-2 data permitted to map local crop differences at field level in 2016 and 2017 years. With reference to MODIS data, the average phenological behavior of main crop classes in the area, obtained from the CORINE Land Cover map Level 3, was considered using a time series decomposition approach. Trend analyses showed that the most of the crop classes alternated three phases (about 7 years) suggesting that, presently, this is probably the time horizon to be considered to tune mid-term algorithms for risk estimates in the agricultural context. Crop classes trends were consequently split into three phases and each of them modeled by a first-order polynomial function used to update correspondent insurance risk rate. Sentinel-2 data were used to map phenological anomalies at field level for the 2016 and 2017 growing seasons; shifts from class average behavior were considered to locally and temporarily tune insurance premium around its average trend as described at the previous step. Synthesizing, one can say that this approach, integrating MODIS and Sentnel-2 data, makes possible to locally and temporarily calibrate premiums of indexed insurance policies by describing the average trends of crop performance (NDVI) at regional level by MODIS data and refining it at field and specific crop level by Sentinel-2 data.  相似文献