Elevation,slope aspect and integrated nutrient management effects on crop productivity and soil quality in North-west Himalayas,India

On farm bio-resource recycling has been given greater emphasis with the introduction of conservation agriculture specifically withclimate change scenarios in the mid-hills of the north-west Himalaya region（NWHR）. Under this changing scenario, elevation, slope aspect and integrated nutrient management（INM） may affect significantly soil quality and crop productivity. A study was conducted during 2009-2010 to 2010-2011 at the Ashti watershed of NWHR in a rainfed condition to examine the influence of elevation, slope aspect and integrated nutrient management（INM） on soil resource and crop productivity. Two years of farm demonstration trials indicated that crop productivity and soil quality is significantly affected by elevation, slope aspect and INM. Results showed that wheat equivalent yield（WEY） of improved technology increased crop productivity by -20%-37% compared to the conventional system. Intercropping of maize with cowpea and soybean enhanced yield by another 8%-17%. North aspect and higher elevation increased crop productivity by 15%-25% compared to south aspect and low elevation（except paddy）. Intercropping of maize with cowpea and soybean enhanced yield by another 8%-15%. Irrespective of slope, elevation and cropping system, the WEY increased by -30% in this region due to INMtechnology. The influence of elevation, slope aspect and INM significantly affected soil resources（SQI） and soil carbon change（SCC）. SCC is significantly correlated with SQI for conventional（R2 = 0.65＊）, INM technology（R2 = 0.81＊） and for both technologies（R2 = 0.73＊）. It is recommended that at higher elevation.（except for paddy soils） with a north facing slope, INM is recommended for higher crop productivity; conservation of soil resources is recommended for the mid hills of NWHR; and single values of SCC are appropriate as a SQI for this region.     

广东近40年来5～9用气候变化及与主要农作物关系

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．     

A meta-analysis of global crop water productivity of three leading world crops (wheat,corn, and rice) in the irrigated areas over three decades

ABSTRACT

The overarching goal of this study was to perform a comprehensive meta-analysis of irrigated agricultural Crop Water Productivity (CWP) of the world’s three leading crops: wheat, corn, and rice based on three decades of remote sensing and non-remote sensing-based studies. Overall, CWP data from 148 crop growing study sites (60 wheat, 43 corn, and 45 rice) spread across the world were gathered from published articles spanning 31 different countries. There was overwhelming evidence of a significant increase in CWP with an increase in latitude for predominately northern hemisphere datasets. For example, corn grown in latitude 40–50° had much higher mean CWP (2.45?kg/m³) compared to mean CWP of corn grown in other latitudes such as 30–40° (1.67?kg/m³) or 20–30° (0.94?kg/m³). The same trend existed for wheat and rice as well. For soils, none of the CWP values, for any of the three crops, were statistically different. However, mean CWP in higher latitudes for the same soil was significantly higher than the mean CWP for the same soil in lower latitudes. This applied for all three crops studied. For wheat, the global CWP categories were low (≤0.75?kg/m³), medium (>0.75 to <1.10?kg/m³), and high CWP (≥1.10?kg/m³). For corn the global CWP categories were low (≤1.25?kg/m³), medium (>1.25 to ≤1.75?kg/m³), and high (>1.75?kg/m³). For rice the global CWP categories were low (≤0.70?kg/m³), medium (>0.70 to ≤1.25?kg/m³), and high (>1.25?kg/m³). USA and China are the only two countries that have consistently high CWP for wheat, corn, and rice. Australia and India have medium CWP for wheat and rice. India’s corn, however, has low CWP. Egypt, Turkey, Netherlands, Mexico, and Israel have high CWP for wheat. Romania, Argentina, and Hungary have high CWP for corn, and Philippines has high CWP for rice. All other countries have either low or medium CWP for all three crops. Based on data in this study, the highest consumers of water for crop production also have the most potential for water savings. These countries are USA, India, and China for wheat; USA, China, and Brazil for corn; India, China, and Pakistan for rice. For example, even just a 10% increase in CWP of wheat grown in India can save 6974 billion liters of water. This is equivalent to creating 6974 lakes each of 100?m³ in volume that leads to many benefits such as acting as ‘water banks’ for lean season, recreation, and numerous ecological services. This study establishes the volume of water that can be saved for each crop in each country when there is an increase in CWP by 10%, 20%, and 30%.     

星载微波SSM/Ⅰ遥感在中国东北华北农田的辐射特征分析

国家遥感应用工程技术研究中心ＮａｔｉｏｎａｌＥｎｇｉｎｅｅｒｉｎｇＲｅｓｅａｒｃｈＣｅｎｔｅｒｆｏｒＧｅｏｍａｔｉｃｓ（ＮＣＧ）国家遥感应用工程技术研究中心于１９９６年１２月２５日由国家科委正式批准组建（国科发计字［１９９６］６０３号文件）。中国科学...     

Simulation Study of the Climate Change Impact on the Rice and its Adaptability in Ningxia Province

Yinhuang Irrigation District in Ningxia, as the top rice production area of high quality and quantity, has a long history in rice planting. The studies of the effective measures for the rice production replying the climate change were very important for reducing the harm of the future climate change and crop supply safety in Ningxia Province. Based on the coupling of the PRECIS model and the crop model CERES Rice, the effects of climate change on the rice production and growth stage in Yinhuang Irrigation District in Ningxia Province were simulated and evaluated, and the adaptability measures of rice production were studied. The results showed that the CERES Rice model had the preferable simulation capability, and the modified PRECIS model also could preferably simulate the required climate parameter. The crop model simulation results showed that the climate change had some influence on the rice production and growth stage in Yinhuang Irrigation District. The rice production goes down under future climate change scenarios in Ningxia Province. The trend of reduction of 2050s is more apparent than that of 2020s under the same scenarios,but the spatial change trend is similar. The extent and range of reduction of A2 scenario are wider than that of B2 scenario in the same period, but spatial change trend is different. For the change of growth stage, there has no obvious change in the north and the central part of the Yinhuang Irrigation District. The duration in 2050s shortens more obviously than that of 2020s under the same scenario, and the duration under B2 scenario shortens more obviously than that under A2 scenario in the same period. The results of adjusting the sowing date and the rice variety parameter G4 showed that the negative impact of climate change on the rice production can be reduced by sowing date advance in Yinhuang Irrigation District in Ningxia Province. There has obvious difference for the optimal G4 values of different region in Yinhuang Irrigation District, and the rice production can also be effectively upraised by adjusting the rice variety characteristic and cultivating the heat resistant rice varieties. The optimal G4 values can mitigate the damage of climate change on the rice production in Yinhuang Irrigation District in Ningxia Province.     

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.     

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.     

干旱半干旱地区农田土壤NO3-N深层积累及其影响因素

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.     

Retrieval of wheat leaf area index from AWiFS multispectral data using canopy radiative transfer simulation

Accurate representation of leaf area index (LAI) from high resolution satellite observations is obligatory for various modelling exercises and predicting the precise farm productivity. Present study compared the two retrieval approach based on canopy radiative transfer (CRT) method and empirical method using four vegetation indices (VI) (e.g. NDVI, NDWI, RVI and GNDVI) to estimate the wheat LAI. Reflectance observations available at very high (56 m) spatial resolution from Advanced Wide-Field Sensor (AWiFS) sensor onboard Indian Remote Sensing (IRS) P6, Resourcesat-1 satellite was used in this study. This study was performed over two different wheat growing regions, situated in different agro-climatic settings/environments: Trans-Gangetic Plain Region (TGPR) and Central Plateau and Hill Region (CPHR). Forward simulation of canopy reflectances in four AWiFS bands viz. green (0.52–0.59 μm), red (0.62–0.68 μm), NIR (0.77–0.86 μm) and SWIR (1.55–1.70 μm) were carried out to generate the look up table (LUT) using CRT model PROSAIL from all combinations of canopy intrinsic variables. An inversion technique based on minimization of cost function was used to retrieve LAI from LUT and observed AWiFS surface reflectances. Two consecutive wheat growing seasons (November 2005–March 2006 and November 2006–March 2007) datasets were used in this study. The empirical models were developed from first season data and second growing season data used for validation. Among all the models, LAI-NDVI empirical model showed the least RMSE (root mean square error) of 0.54 and 0.51 in both agro-climatic regions respectively. The comparison of PROSAIL retrieved LAI with in situ measurements of 2006–2007 over the two agro-climatic regions produced substantially less RMSE of 0.34 and 0.41 having more R² of 0.91 and 0.95 for TGPR and CPHR respectively in comparison to empirical models. Moreover, CRT retrieved LAI had less value of errors in all the LAI classes contrary to empirical estimates. The PROSAIL based retrieval has potential for operational implementation to determine the regional crop LAI and can be extendible to other regions after rigorous validation exercise.     

Early-season mapping of crops and cultural operations using very high spatial resolution Pléiades images

The aim of this study was to assess the contribution of very high spatial resolution (VHSR) Pléiades images to both early season crop identification and the mapping of bare soil surface characteristics due to cultural operations. The study region covering 21 km² is located west of the peri-urban territory of the Versailles plain and the Alluets plateau (Yvelines, France). About 100 cropped fields were observed on the ground synchronously with two Pléiades images of 3 and 24 April 2013 and one SPOT4 image of 2 April 2013. The GIS structuring of these field data along with vector information about field boundaries was used for delimitating both training and test zones for the support vector machine classifier with polynomial function kernel (pSVM). The pSVM was computed on the spectral bands and NDVI for both single-date Pléiades and the bi-temporal Pléiades pair. For the single-date classifications of crops, the overall per-pixel accuracy reached 87% for the SPOT4 image of 2 April (6 classes), 79% for the Pléiades image of 3 April (6 classes) and 82% for that of 24 April (7 classes). At the earlier date (2–3 April), the Pléiades image very well discriminated cultural operations (>77%, user’s or producer’s accuracies) as well as fallows and grasslands, while winter cereals and rapeseed were better discriminated by the SPOT4 image winter cereals (>70%, user’s or producer’s accuracies). As Pléiades images revealed within-field spatial variations of early phenological stages of winter cereals that could be critical for adjusting management of zones with delayed development during the growing season, they brought information complementary to multispectral images with high spatial resolution. For the bi-temporal Pléiades image, the overall per-pixel accuracy was about 80% (7 classes), winter crops, grasslands and fallows being very well detected while confusions occurred between spring barley at initial stages (2–3 leaves) and bare soils prepared for other spring crops. Using an additional validation field set covering ∼1/3 of the study area croplands, the crop map resulting from the bi-temporal Pléiades pair achieved correct crop prediction for about 89.7% of the validation fields when considering composite classes for winter cereals and for spring crops. Early-season Pléiades images therefore show a considerable potential for anticipating regional crop patterns and detecting soil tillage operations in spring.