1971—2010年气候变化对河南省主要作物需水量的影响

以小麦、玉米、棉花、花生等主要农作物为研究对象,选取河南省12个气象站点1971—2010年的气象资料,采用联合国粮农组织（FAO）推荐的Penman-Monteith公式计算参考作物生育期内需水量,分析其生育期需水量变化规律.结果表明：河南省多个地区需水量的年内分布,都在6月达到全年的最高值,在11或12月降至全年的最低值;需水量最大的是棉花,其次是小麦、花生和玉米;近40年,棉花、小麦、花生和玉米的需水量都表现为减少趋势.通过分析各气象因子与需水量的相关性发现：平均风速与作物需水量显著正相关,由于平均风速大幅下降,从而在很大程度上抵消了因其他气象因子变化引起的需水量增加趋势,造成主要作物生育期需水量的减少.     

地下水埋深对作物产量与水分利用效率的影响及作物系数变化

采用排水式蒸渗仪试验,研究不同地下水埋深对冬小麦和春玉米产量和水分利用效率的影响及其作物系数的变化,表明冬小麦和春玉米生育期的地下水位分别控制在1.5 m和1.0 m以下时产量和水分利用效率最为适宜,作物系数随地下水埋深的变化而变化,幅度可达到0.5～0.6,作物系数最大值处于1.2 m埋深处。研究结果可为地下水埋深较...     

农作物单产预测的运行化方法

提出了适于运行化农作物单产预测的方法。即以农作物单产区划为基础 ,通过搜集不同地区不同作物的单产预测模型 ,分析每个模型的空间适用范围 ,并从模型参数等角度筛选模型 ,然后利用这些模型进行气象站点的作物单产预测 ,并以NDVI分布图为参考数据将点上的单产数据空间外推到区域尺度。借助耕地分布估计区域水平的农作物单产。最后以 2 0 0 3年冬小麦为例 ,进行了全国 10个省的冬小麦平均单产估算 ,花费了较少的人力和时间 ,符合运行化遥感估产要求     

春季低温连阴雨灾害对农作物产量影响评估

依据河南省春季低温连阴雨灾害标准,对信阳市1971—2007年春季达到低温连阴雨过程标准的气象资料,分别从3月低温上,4月低温、连阴雨以及低温连阴雨,5月连阴雨等不同的致灾因子持续时间长短、对农作物造成危害程度的大小,将其划分3成个不同的灾害等级。用拉格朗日插值法计算作物的期望产量,用分离法将春季低温连阴雨灾害对作物造成的损失分离出来。结果发现,不同时段的春季低温连阴雨对水稻、小麦、油菜和茶叶造成的危害程度是不同的,以4月低温连阴雨和5月连阴雨危害最大。定量评估春季低温连阴雨灾害损失对防灾减灾和政府决策具有十分重要意义。     

Impact of Climate Change on Maize Potential Productivity and the Potential Productivity Gap in Southwest China

The impact of climate change on maize potential productivity and the potential productivity gap in Southwest China(SWC) are investigated in this paper.We analyze the impact of climate change on the photosynthetic,light-temperature,and climatic potential productivity of maize and their gaps in SWC,by using a crop growth dynamics statistical method.During the maize growing season from 1961 to 2010,minimum temperature increased by 0.20℃ per decade(p 0.01) across SWC.The largest increases in average and minimum temperatures were observed mostly in areas of Yunnan Province.Growing season average sunshine hours decreased by 0.2 h day~(-1) per decade(p 0.01) and total precipitation showed an insignificant decreasing trend across SWC.Photosynthetic potential productivity decreased by 298 kg ha~(-1)per decade(p 0.05).Both light-temperature and climatic potential productivity decreased(p 0.05) in the northeast of SWC,whereas they increased(p 0.05) in the southwest of SWC.The gap between lighttemperature and climatic potential productivity varied from 12 to 2729 kg ha~(-1),with the high value areas centered in northern and southwestern SWC.Climatic productivity of these areas reached only 10%-24%of the light-temperature potential productivity,suggesting that there is great potential to increase the maize potential yield by improving water management in these areas.In particular,the gap has become larger in the most recent 10 years.Sensitivity analysis shows that the climatic potential productivity of maize is most sensitive to changes in temperature in SWC.The findings of this study are helpful for quantification of irrigation water requirements so as to achieve maximum yield potentials in SWC.     

Monitoring US agriculture: the US Department of Agriculture,National Agricultural Statistics Service,Cropland Data Layer Program

The National Agricultural Statistics Service (NASS) of the US Department of Agriculture (USDA) produces the Cropland Data Layer (CDL) product, which is a raster-formatted, geo-referenced, crop-specific, land cover map. CDL program inputs include medium resolution satellite imagery, USDA collected ground truth and other ancillary data, such as the National Land Cover Data set. A decision tree-supervised classification method is used to generate the freely available state-level crop cover classifications and provide crop acreage estimates based upon the CDL and NASS June Agricultural Survey ground truth to the NASS Agricultural Statistics Board. This paper provides an overview of the NASS CDL program. It describes various input data, processing procedures, classification and validation, accuracy assessment, CDL product specifications, dissemination venues and the crop acreage estimation methodology. In general, total crop mapping accuracies for the 2009 CDLs ranged from 85% to 95% for the major crop categories.     

Vegetation monitoring for Guatemala: a comparison between simulated VIIRS and MODIS satellite data

The advanced very high resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) data are being widely used for vegetation monitoring across the globe. However, sensors will discontinue collecting these data in the near future. National Aeronautics and Space Administration is planning to launch a new sensor, visible infrared imaging radiometer suite (VIIRS), to continue to provide satellite data for vegetation monitoring. This article presents a case study of Guatemala and compares the simulated VIIRS-Normalized Difference Vegetation Index (NDVI) with MODIS-NDVI for four different dates each in 2003 and 2005. The dissimilarity between VIIRS-NDVI and MODIS-NDVI was examined on the basis of the percent difference, the two-tailed student's t-test, and the coefficient of determination, R ². The per cent difference was found to be within 3%, the p-value ranged between 0.52 and 0.99, and R ² exceeded 0.88 for all major types of vegetation (basic grains, rubber, sugarcane, coffee and forests) found in Guatemala. It was therefore concluded that VIIRS will be almost equally capable of vegetation monitoring as MODIS.     

Coupling the 4M crop model with national geo-databases for assessing the effects of climate change on agro-ecological characteristics of Hungary

The 4M crop model was used to investigate the prospective effects of climate change on the agro-ecological characteristics of Hungary. The model was coupled with a detailed meteorological database and spatial soil information systems covering the whole territory of Hungary. Plant-specific model parameters were determined by inverse modeling. Future meteorological data were produced from the present meteorological data by combining a climate change scenario and a stochastic weather generator. Using the available and the generated data, the present and the prospective agro-ecological characteristics of Hungary were determined. According to the simulation results, average yields will decrease considerably (~30%) due to climate change. The rate of nitrate leaching will prospectively decrease as well. The fluctuations of both the yields and the annual nitrate leaching rates will most likely increase approaching the end of the twenty-first century. On the basis of the simulation results, the role of autumn crops is likely to become more significant in Hungary. The achieved results can be generalized for more extended regions based on the concept of spatial (geographical) analogy.     

Archeological crop marks identified from Cosmo-SkyMed time series: the case of Han-Wei capital city,Luoyang, China

ABSTRACT

The development of spaceborne Synthetic Aperture Radar (SAR) technology declares that the golden era of SAR remote sensing in archeology is approaching; however, nowadays its methodology framework is still lacking due to the inadequate case studies validated by ground-truths. In this study, we investigated the crop marks using multi-temporal Cosmo-SkyMed data acquired in 2013 by applying a two-step decision-tree classifier in conjunction with a spatial analysis in an area of archeological interest nearby the archeological site of Han-Wei capital city (1900–1500 BP), in Luoyang, China. The time-series backscattering anomalies related to the wheat growth cycle were identified and then further validated in two zones by geophysical investigations (Ground Penetration Radar and electrical measurements) and in a third zone by archeological excavations made after the SAR data acquisition. This study provides a new approach for the relic detection, shallowly buried and covered by the crop vegetation, by temporal crop marks on spaceborne SAR images. We also emphasize the necessity to establish a satellite-to-ground methodology framework for the promotion of remote-sensing technology in archeology.     

Quantifying the impact of no-till on sediment yield in southern Brazil at the hillslope and catchment scales

No-till (NT) is a soil management system designed to protect soil resources from water erosion and provide numerous benefits compared to conventional tillage through the increase of organic matter inputs into the soil. However, NT in isolation is not sufficient to control erosion processes caused by an excessive production of surface runoff. This study evaluated soil losses on agricultural hillslopes under no-till characterized by contrasted water, soil, and crop management conditions. To this end, water and soil losses were monitored between 2014 and 2018 at two scales, including four macroplots (0.6 ha; 27 events) and two paired zero-order catchments (2.4 ha; 63 events). The resulting dataset covered a wide range of rainfall conditions that occurred in contrasted soil, crop, and runoff management conditions. Hyetographs, hydrographs, and sedigraphs were constructed, and these data were used to evaluate the impact of management on sediment yields, including that of terraces, scarification, and phytomass on sediment yield. The installation of terraces reduced sediment yield by 58.7%, mainly through surface runoff control. Crop management including an increased phytomass input efficiently controlled soil losses (63%), although it did not reduce runoff volume and peak flow. In contrast, scarification had no impact on runoff and soil losses. The current research demonstrated the need to combine the installation of terraces and leaving a high amount of phytomass on the soil to control surface runoff and erosion and reduce sediment yield. The current research therefore reinforces the relevance of the monitoring strategy conducted at the scale of macroplots and zero-order catchments to evaluate the impact of contrasted water, soil, and crop management methods and select the most effective conservation agriculture practices.