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

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.     

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

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.     

基于遥感信息的华北冬小麦区域生长模型及模拟研究

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。     

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

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.     

An Early Cretaceous extinct spreading center in the northern Natal valley

We have identified an extinct E–W spreading center in the northern Natal valley on the basis of magnetic anomalies which was active from chron M11 (133 Ma) to 125.3 Ma, just before chron M2 (124 Ma) in the Early Cretaceous. Seafloor spreading in the northern Natal valley accounts for approximately 170 km of north–south motion between the Mozambique Ridge and Africa. This extension resolves the predicted overlap of the continental (central and southern) Mozambique Ridge and Antarctica in the chron M2 to M11 reconstructions from Mesozoic finite rotation parameters for Africa and Antarctica. In addition, the magnetic data reveal that the Mozambique Ridge was an independent microplate from at least 133 to 125 Ma. The northern Natal valley extinct spreading center connects to the spreading center separating the Mozambique Basin and the Riiser-Larsen Sea to the east. It follows that the northern Mozambique Ridge was either formed after the emplacement of the surrounding oceanic crust or it is the product of a very robust spreading center. To the west the extinct spreading center connects to the spreading center separating the southern Natal valley and Georgia Basin via a transform fault. Prior to chron M11, there is still a problem with the overlap of Mozambique Ridge if it is assumed to be fixed with respect to either the African or Antarctic plates. Some of the overlap can be accounted for by Jurassic deformation of the Mozambique Ridge, Mozambique Basin, and Dronning Maud land. It appears though that the Mozambique Ridge was an independent microplate from the breakup of Gondwana, 160 Ma, until it became part of the African plate, 125 Ma.     

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

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.     

Application of S-SEBI model for crop evapotranspiration using Landsat-8 data over parts of North India

Estimation and monitoring of crop evapotranspiration (ET_c) or consumptive water use over large-area holds the key to irrigation management plans and regional drought preparedness. The objective of this study was to estimate ET_c by applying the simplified-surface energy balance index (S-SEBI) model to Landsat-8 data for the 2014–2015 period in parts of North India. An average ET_c was estimated 2.72 and 2.47 in mm day^?1 with 0.22, 0.18 standard deviation and 0.11, 0.07 standard error for Kharif and Rabi crops, respectively. On validation part, a close relationship was observed between S-SEBI derived and scintillometer observed evaporative fraction with 0.85 correlation coefficient and 0.86 agreement index. The statistical analysis also endorses the results accuracy and reliability with 0.026 and 0.602, relative root-mean square errors and model efficiency for wheat crop, respectively. The study showed that normalized difference vegetation index and LST are closely related and serve as a proxy for qualitative representation of ET_c.     

In-season crop classification using elements of the Kennaugh matrix derived from polarimetric RADARSAT-2 SAR data

Accurate spatio-temporal classification of crops is of prime importance for in-season crop monitoring. Synthetic Aperture Radar (SAR) data provides diverse physical information about crop morphology. In the present work, we propose a day-wise and a time-series approach for crop classification using full-polarimetric SAR data. In this context, the 4 × 4 real Kennaugh matrix representation of a full-polarimetric SAR data is utilized, which can provide valuable information about various morphological and dielectric attributes of a scatterer. The elements of the Kennaugh matrix are used as the parameters for the classification of crop types using the random forest and the extreme gradient boosting classifiers.The time-series approach uses data patterns throughout the whole growth period, while the day-wise approach analyzes the PolSAR data from each acquisition into a single data stack for training and validation. The main advantage of this approach is the possibility of generating an intermediate crop map, whenever a SAR acquisition is available for any particular day. Besides, the day-wise approach has the least climatic influence as compared to the time series approach. However, as time-series data retains the crop growth signature in the entire growth cycle, the classification accuracy is usually higher than the day-wise data.Within the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative, in situ measurements collected over the Canadian and Indian test sites and C-band full-polarimetric RADARSAT-2 data are used for the training and validation of the classifiers. Besides, the sensitivity of the Kennaugh matrix elements to crop morphology is apparent in this study. The overall classification accuracies of 87.75% and 80.41% are achieved for the time-series data over the Indian and Canadian test sites, respectively. However, for the day-wise data, a ∼6% decrease in the overall accuracy is observed for both the classifiers.     

Evaluating assumptions for seismic assessment of existing buildings

This paper evaluates the American FEMA 356 and the Greek GRECO (EC 8 based) procedural assumptions for the assessment of the seismic capacity of existing buildings via pushover analyses. Available experimental results from a four-storeyed building are used to compare the two different sets of assumptions. If the comparison is performed in terms of initial stiffness or plastic deformation capacities, the different partial assumptions of the procedures lead to large discrepancies, while the opposite occurs when the comparison is performed in terms of structural performance levels at target displacements. According to FEMA 356 assumptions, effective yield point rigidities are approximately four times greater than those of EC 8. Both procedures predicted that the structure would behave elastically during low-level excitation and that the structural performance level at target displacement for a high-level excitation would be between the Immediate Occupancy and Life Safety performance levels.     

农业生态的化学元素分类及其与作物优质高产的关系

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。