Slope-climbing of cropland and its effects in China

In recent decades,the spatio-temporal patterns of China’s croplands have been reshaped by disturbances from anthropogenic activities,with complex changes in the topographic characteristics of croplands.Slope-climbing of croplands(SCCL)is an important issue that threatens sustainable agricultural development.While providing land with prominent location advantages,SCCL weakens the water and fertilizer retention capacity for cropland,intensifies various geological disasters,and adversely affects the ecological environment and food yield of these croplands.It is crucial to determine the spatio-temporal variation features and effects of SCCL in China to formulate more accurate cropland protection policies and to maintain food security;however,the current lack of relevant studies is detrimental for capturing trends in cropland resources and sustainable cropland use.In this study,we constructed a multi-scale slope spectrum for cropland and total terrain to explore the spatial differences and trends of SCCL from a three-dimensional view.We evaluated the natural and socioeconomic effects of SCCL in China from multiple perspectives.Results indicate that the proportion of cropland with slopes below 2°,5°,and 6°in China decreased by 0.43%,0.47%,and 0.50%from 1980 to 2020,respectively.SCCL became apparent during 1980-1990 and 2010-2020,especially over the recent decade.The cropland climbing index(CCI)and upper limited slope change(ULSC)to measure the spatio-temporal pattern of SCCL were 0.99%and 1.17°,respectively,during 2010-2020.At the agricultural regional scale,the SCCL was also concentrated in 1980-1990 and 2010-2020,and it is more pronounced in the southern areas.The proportion of provinces and prefecture-level cities with high-intensity SCCL during 1980-2020 were 87.10%and 49.73%,respectively.SCCL was comparatively more pronounced and broader from 2010 to 2020.During this period,17.84%of prefecture-level cities had no SCCL,and the average CCI for all prefecture-level cities peaked at 1.62%.In this study,we also evaluated the pros and cons of SCCL and provided targeted suggestions for decision makers and farmers to refine cropland protection policy systems and further develop the sustainable use of croplands.

44%of steep slope cropland in Europe vulnerable to drought

Steep-slope cropland plays a vital role in food production,economic development,ecosystem diversity,and Eu-ropean cultural heritage.However,these systems are susceptible to extreme weather events.The 2022 summer drought significantly impacted European agriculture,but the specific effects on steep-slope crops remain uncer-tain.Clarifying this is essential for comprehending similar future events and for implementing effective water management strategies to ensure the sustainability of steep-slope agriculture and associated ecosystem services.This study quantitatively analyzes the spatial distribution of twelve major European steep-slope(>12%)crops and assesses agricultural drought severity during the 2022 events using open-access spatial data.The satellite-based Vegetation Health Index(VHI)is utilized to identify critical hotspots.Results show that olive grove is the most widespread crop in steep slope agriculture(34%of total area),followed by wheat(24%),maize(16%),and vineyard(11%).Almost half of the steep-slope agriculture in Europe suffered drought during summer 2022.Vineyards were hardest affected at 79%,primarily in northern Portugal,northern Spain,southern France,and central Italy.Sunflowers followed at 62%,mainly in Spain,central Italy,southern France,and northern Roma-nia.Olive groves ranked third at 59%,with the most impact in northern Portugal,southern and central Spain,and southern Italy.Maize was also significantly affected at 54%.In this paper,we therefore highlight the need to increase steep-slope agriculture resilience by improving water management and promoting sustainable land practices.

Quantifying the agreement and accuracy characteristics of four satellite-based LULC products for cropland classification in China

Various land use and land cover(LULC)products have been produced over the past decade with the development of remote sensing technology.Despite the differences in LULC classification schemes,there is a lack of research on assessing the accuracy of their application to croplands in a unified framework.Thus,this study evaluated the spatial and area accuracies of cropland classification for four commonly used global LULC products(i.e.,MCD12Q1V6,GlobCover2009,FROM-GLC and GlobeLand30)based on the harmonised FAO criterion,and quantified the relationships between four factors(i.e.,slope,elevation,field size and crop system)and cropland classification agreement.The validation results indicated that MCD12Q1 and GlobeLand30 performed well in cropland classification regarding spatial consistency,with overall accuracies of 94.90 and 93.52%,respectively.The FROMGLC showed the worst performance,with an overall accuracy of 83.17%.Overlaying the cropland generated by the four global LULC products,we found the proportions of complete agreement and disagreement were 15.51 and 44.72% for the cropland classification,respectively.High consistency was mainly observed in the Northeast China Plain,the Huang-Huai-Hai Plain and the northern part of the Middle-lower Yangtze Plain,China.In contrast,low consistency was detected primarily on the eastern edge of the northern and semiarid region,the Yunnan-Guizhou Plateau and southern China.Field size was the most important factor for mapping cropland.For area accuracy,compared with China Statistical Yearbook data at the provincial scale,the accuracies of different products in descending order were:GlobeLand30,FROM-GLC,MCD12Q1,and GlobCover2009.The cropland classification schemes mainly caused large area deviations among the four products,and they also resulted in the different ranks of spatial accuracy and area accuracy among the four products.Our results can provide valuable suggestions for selecting cropland products at the national or provincial scale and help cropland mapping and reconstruction,which is essential for food security and crop management,so they can also contribute to achieving the Sustainable Development Goals issued by the United Nations.

Decoding the inconsistency of six cropland maps in China

Accurate cropland information is critical for agricultural planning and production,especially in foodstressed countries like China.Although widely used medium-to-high-resolution satellite-based cropland maps have been developed from various remotely sensed data sources over the past few decades,considerable discrepancies exist among these products both in total area and in spatial distribution of croplands,impeding further applications of these datasets.The factors influencing their inconsistency are also unknown.In this study,we evaluated the consistency and accuracy of six cropland maps widely used in China in circa 2020,including three state-of-the-art 10-m products(i.e.,Google Dynamic World,ESRI Land Cover,and ESA WorldCover)and three 30-m ones(i.e.,GLC_FCS30,GlobeLand 30,and CLCD).We also investigated the effects of landscape fragmentation,climate,and agricultural management.Validation using a ground-truth sample revealed that the 10-m-resolution WorldCover provided the highest accuracy(92.3%).These maps collectively overestimated Chinese cropland area by up to 56%.Up to 37%of the land showed spatial inconsistency among the maps,concentrated mainly in mountainous regions and attributed to the varying accuracy of cropland maps,cropland fragmentation and management practices such as irrigation.Our work shed light on the promotion of future cropland mapping efforts,especially in highly inconsistent regions.

Improving model performance in mapping cropland soil organic matter using time-series remote sensing data

Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management policies.As a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different scales.However,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance.To overcome this limitation,this study systematically assessed a framework of“information extractionfeature selection-model averaging”for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in 2021.The results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R^(2):0.48 to 0.53)without having obviously negative impacts on uncertainty.Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R^(2)of random forest from 0.44 to 0.48 and the R^(2)of extreme gradient boosting from 0.37to 0.43.Forward recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(<200),whereas Boruta is recommended when there are many environmental covariates(>500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty.When the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final predictions.Given the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales,so this approach can provide more reliable references for soil conservation policy-making.

Coupling Coordination Analysis of Cropland Intensification and Agroecosystem Services:Evidence from Loess Plateau in Shaanxi Province,China

One of the greatest challenges in the agroecosystem is to improve cropland intensification while preserving agroecosystem services.While many studies have investigated the effect of cropland intensification on agroecosystem service,the interactive coupling and coordination among these factors remain largely unexplored.In view of this,this study performed a case study of the Loess Plateau in Shaanxi Province,China and constructed comprehensive evaluation models to quantify the cropland intensification and agroecosystem service in this area.Balance analysis and the coupling coordination degree model were used to evaluate the interactive relationship between cropland intensification and agroecosystem service,and statistical analysis and spatial autocorrelation were used to analyze the spatial characteristics and potential mechanism of the coupling coordination.Results show that both the cropland intensification and agroecosystem service in the study area were relatively low yet gradually increased from 2000 to 2020.Agroecosystem service lag was identified as the dominant unbalanced development type.Improving the supply capacity of agroecosystem services plays a key role in the balanced development of cropland in the Loess Plateau.The coupling coordination degree between cropland intensification and agroecosystem service ranges from basic coordination to serious incoordination.Therefore,cropland intensification practices in the area should be optimized to enhance this coordination degree.An upward trend was also observed in the coupling coordination degree from2000 to 2020.The withdrawal of marginal cropland in the Grain for Green program is one of the most important reasons for this trend,especially for the northern region.Around 83.6%of the high-high clusters are concentrated in the southern region of the Loess Plateau,whereas 70.5%of the low-low clusters are distributed in the northern region.These clustering characteristics are mainly attributed to the environmental suitability of these areas for agriculture and their degree of economic development.

Uphill or downhill?Cropland use change and its drivers from the perspective of slope spectrum

The continuous decrease of low-slope cropland resources caused by construction land crowding poses huge threat to regional sustainable development and food security.Slope spectrum analysis of topographic and geomorphic features is considered as a digital terrain analysis method which reflects the macro-topographic features by using micro-topographic factors.However,pieces of studies have extended the concept of slope spectrum in the field of geoscience to construction land to explore its expansion law,while research on the slope trend of cropland from that perspective remains rare.To address the gap,in virtue of spatial analysis and geographically weighted regression(GWR)model,the cropland use change in the Yangtze River Basin(YRB)from 2000 to 2020 was analyzed and the driving factors were explored from the perspective of slope spectrum.Results showed that the slope spectrum curves of cropland area-frequency in the YRB showed a first upward then a downward trend.The change curve of the slope spectrum of cropland in each province(municipality)exhibited various distribution patterns.Quantitative analysis of morphological parameters of cropland slope spectrum revealed that the further down the YRB,the stronger the flattening characteristics,the more obvious the concentration.The province experienced the greatest downhill cropland climbing(CLC)was Shannxi,while province experienced the highest uphill CLC was Zhejiang.The most common cropland use change type in the YRB was horizontal expansion type.The factors affecting average cropland climbing index(ACCI)were quite stable in different periods,while population density(POP)changed from negative to positive during the study period.This research is of practical significance for the rational utilization of cropland at the watershed scale.

Exploring the rules of cropland elevation uplift in China

Cropland elevation uplift(CLEU) has recently become a new challenge for agricultural modernization,food security,and sustainable cropland use in China.Uncovering the rules of CLEU is of great theoretical and practical significance for China’s sustainable agricultural development and rural revitalization strategy.However,existing studies lack in-depth disclosure of multi-scale CLEU evolution rules,making it difficult to support the formulation of specific cropland protection policies.We analyzed the spatio-temporal evolution and multiscale CLEU in China from 1980 to 2020 using the Lorenz curve,gravity center model,hotspot analysis,and cropland elevation spectrum.The results indicated that the center of gravity of cropland moved to the northeast from 1980 to 2000 and then shifted to the northwest.The spatial distribution of cropland became increasingly imbalanced from 1980 to 2000.The change hotspots clustered in the northwest and the northeast,whereas cold-spots were mainly in southeastern China.The average elevation of cropland increased by 17.38 m,and the elevation uplift rule in different regions differed evidently across scales.From 1980 to 2000,all provinces except Xinjiang,Inner Mongolia,Gansu,and Yunnan exhibited CLEU,with Qinghai,Tibet,Beijing,and Guangdong showing the most noticeable uplifting.The CLEU can alleviate the shortage of cropland to some extent.However,without a planning constraint,the CLEU will lead to the increase of ecological risk and food security risk.

Untangling the increasing elevation of cropland in China from 1980 to 2020

The redistribution of cropland to areas of higher elevation in China has long affected agricultural development and could seriously threaten national food security.However,there is currently little research reported on this phenomenon,which may limit the improvement of cropland protection policies.To fill this gap,we analyzed the spatiotemporal characteristics and driving mechanisms of increased cropland elevation in China during the period 1980-2020.The average cropland elevation in China increased by 17.38 m from 1980 to 2020.The gravity center of the cropland area and average cropland elevation in China moved to the northwest by 81.00 km and 51.47 km,respectively.The amount of newly added cropland in eastern China was less than that in occupied regions;however,the average elevation of newly added cropland was greater than that of occupied cropland,though the opposite phenomenon was observed in western China.Slope,temperature,land-use intensity,population,economic density,and distance to main roads were the main factors affecting the redistribution of cropland to areas of higher elevation.The effects of these major driving factors exhibited significant spatial and temporal variations in China.This study has important implications for improving existing cropland protection policies and developing more effective cropland management systems in China.

农牧交错区不同撂荒年限对农田土壤的影响

为研究撂荒年限对农田土壤的影响,本试验于8月植物生长旺季进行,选取未撂荒农田(CK)、撂荒7年(7a)、15年(15a)和30年(30a)的农田采集土壤样品,室内计算分析土壤理化特征的变化规律,结果表明,撂荒显著提高了土壤容重,降低了土壤孔隙度和土壤pH。与未撂荒地对比,撂荒显著提高了土壤有机碳(Soil organic carbon,SOC)、全氮(Soil total nitrogen,TN)和全磷(Soil total phosphorus,TP)的含量。因此,长期撂荒对土壤养分状况具有显著改善作用。不同撂荒年限土壤含水量与土壤养分指标均呈显著正相关,长期撂荒下更少的蒸散耗水量减少了土壤水分的损失,使土壤养分得到了积累。本研究可为坝上农牧交错带撂荒地的合理规划提供理论支撑。

2013-2020年海河流域农田水热碳通量及气象要素观测数据集

本研究以海河流域官厅水库旁农田生态系统为研究对象,采用蒸渗仪、涡动相关仪、大孔径闪烁仪和自动气象站/气象要素梯度等观测系统,开展农田生态系统多尺度通量和气象要素的长期定位观测。本数据集由多尺度通量(米,百米,公里级)和气象要素数据组成,观测项目包括生态系统净碳交换量、潜热通量/蒸散发、感热通量、空气温度、空气相对湿度、风速、风向、向下/上短波辐射、向下/上长波辐射、净辐射、大气压、降水、红外辐射温度、光合有效辐射、土壤温度、土壤水分、土壤热通量、平均土壤温度等。本数据集经过了严格的处理和质量控制,可用于研究水库消涨对周边生态系统碳水等物质和能量的循环带来的影响,也可为相关遥感模型或过程模型等研究提供有力的数据基础。

城乡建设用地增减挂钩政策与城乡收入差距

在空间均衡视角下,建立理论模型分析城乡建设用地增减挂钩政策对城乡收入差距的影响,并通过实证分析进行政策评估的结果表明:增减挂钩政策能够提高城乡居民人均收入,但是对城市居民人均收入提高幅度更多,从而加大城乡收入差距。如果配套实施农村生产率提升的增减挂钩政策,在提高城乡居民人均收入的同时,这种综合性改革能够促使农村人均收入提高幅度更大,进而缩小城乡收入差距。随后,基于2000—2016年省级面板数据,采用逐期双重差分法与合成控制法分别评估城乡建设用地增减挂钩政策与配套实施农村生产率提升的增减挂钩政策对城乡收入差距影响的实证结果发现:自2006年实施增减挂钩政策后,增减挂钩试点省份的城乡收入差距均值提升0.125,达到2.820;自2008年实施配套农村生产率提升的增减挂钩政策后,重庆市的城乡收入差距均值下降0.261,达到3.043。基于此,在城乡融合背景下,我们应建立健全城乡统一建设用地市场制度。

Noah-MP陆面模式对干旱区不同下垫面水热通量模拟评估

基于干旱区张掖国家气候观象台2021年1月-2022年6月和大满灌区绿洲农田站2020年1-12月的观测数据,评估Noah-MP模式对干旱区荒漠和农田两种下垫面的水热通量的模拟性能。结果表明:Noah-MP模式模拟的干旱区荒漠下垫面辐射与观测值的相关系数均>0.98,泰勒评分>0.93。感热的泰勒评分(0.809)>潜热的泰勒评分(0.504),对辐射及湍流通量的变化特征、峰谷值与观测值总体一致,模拟效果较好。模拟的5 cm土壤湿度对降水过程有明显的响应,表现出明显的冷暖季差异,但其模拟性能仍有待改进。Noah-MP模式模拟的干旱区农田下垫面辐射通量及各层土壤温度的相关系数均>0.98,泰勒评分>0.58,模拟效果较理想。但模拟的潜热通量及各层土壤湿度较观测值偏低,尤其在生长季模拟性能不理想。Noah-MP模式对干旱区荒漠下垫面水热通量的模拟性能优于农田下垫面,优化和发展模式水文过程的参数化方案,在模式中考虑人为作用,是提高干旱区陆面过程模式模拟能力的重要方向。

黑土长缓坡地形与横垄对土壤有机碳空间分异的交互作用

横坡垄作对坡耕地土壤有机碳(soil organic carbon,SOC)流失有一定的阻控作用,但黑土区特有长而缓的地形与横垄对坡耕地SOC空间分异会产生交互作用,而这种交互作用引发的SOC流失风险没引起足够的重视。该研究以典型黑土区黑龙江省黑河市北安分局红星农场为研究区域,2022年在横坡垄作与顺坡水线方向上共布设25个采样点,采用地理探测器模型、单因素方差分析(one-way ANOVA)和Pearson相关性分析,探讨土壤有机碳的空间分异及其交互作用。结果表明,横坡垄作方向上垄沟土壤有机碳含量从坡顶到水线呈现逐渐增大的变化趋势;在垄台从坡顶到水线呈现先增大后减小的变化趋势。顺坡水线方向,土壤有机碳含量在垄沟呈现从上坡到下坡增大的变化趋势;在垄台呈现先增大后减小的变化趋势。由于断垄产生水线,顺坡土壤有机碳含量上坡与下坡仍有显著差异(P<0.05)。Pearson相关性分析表明,有机碳与可蚀性K因子呈显著负相关(垄沟和垄台相关系数分别为–0.228和–0.238,P<0.05),与碳循环相关的β-葡萄糖苷酶和微生物生物量碳在垄沟呈极显著正相关(相关系数为0.398和0.676,P<0.01)。地理探测器分析表明,顺坡水线对土壤有机碳空间分异的影响最大,其对垄沟和垄台SOC的解释力分别达到61%和52%以上;顺坡水线与其他因子的交互作用共同增强了对土壤有机碳的解释力,尤其是顺坡水线与高程的交互作用最为明显。黑土区坡耕地土壤有机碳空间分异主要受顺坡水线与高程的交互作用,横坡垄作虽然能够拦截径流,但由于长缓坡地形影响产生的断垄会加剧土壤侵蚀诱发的有机碳流失。因此,黑土坡耕地治理需要同时考虑横垄与地形的共同影响,从而实现防蚀的优化效果。

基于递归特征消除−随机森林模型的江浙沪农田土壤肥力属性制图

以江苏省、浙江省、上海市农田为研究区,选用气候、地形、植被、土壤属性等自然环境协变量,及农业机械总动力、每公顷农用化肥施用量、农业总产值、农村用电量等农业活动变量,利用递归特征消除方法(RFE)对环境协变量进行筛选,基于筛选后的最优变量组合建立随机森林(RF)模型,进行表层土壤pH、有机碳、全氮、全磷、全钾、铵态氮、硝态氮、有效磷、速效钾、交换性钙、交换性镁11种主要土壤肥力属性的空间分布预测,并采用100次重复的十折交叉验证法进行验证。结果表明:①11个模型筛选出的环境协变量类型主要集中在气候、地形与植被变量,表征人类农业活动的变量在有机碳、全磷、全钾、铵态氮和有效磷预测中体现重要作用。②11个模型的决定系数(R^(2))在0.27~0.53,pH、速效钾、交换性镁和交换性钙的预测模型决定系数(R^(2))均在0.45以上。本研究表明人类活动变量对于土壤肥力预测具有重要意义,而递归特征消除−随机森林模型(RFE-RF)可以用于农田主要土壤肥力属性制图,为农业生产提供准确的土壤肥力属性空间分布信息。

中国农田土壤有机碳库及其影响因素研究述评

在全球气候变暖的背景下,土壤在减缓气候变化中的巨大潜能逐渐被人们所重视。农田是受人为因素影响较大又可在较短时间内调节的生态系统,其土壤碳汇能力的充分发挥将有助于减缓全球气候变暖的趋势。文章通过梳理大量已发表文献,综述了中国农田土壤有机碳库自20世纪50年代以来的演变特征,指出中国农田土壤1950-1980年期间以丢碳为主,1980年以后经过农业管理措施的改变,有机碳含量逐渐提升。总结了中国农田固碳潜力相关研究,虽然不同估算方法的出的结果存在一定差异,但总体来看中国农田土壤有较大的固碳潜力。分析了农田土壤有机碳库的影响因素,虽然气候因素和土壤理化性质的差异能对农田土壤有机碳库造成影响,但农业管理措施才是农田土壤有机碳含量产生变化的关键。指出了中国农田土壤有机碳库研究方面存在的问题以及今后的发展方向,为气候变化背景下中国农田土壤碳减排提供了新的参考与借鉴。

基于多季相分形特征的Landsat 8 OLI影像耕地信息提取方法

利用遥感技术快速准确地提取耕地信息是耕地保护的关键环节。以山东省商河县为例,提出了一种基于多季相分形特征的Landsat 8 OLI影像耕地信息提取方法。首先采用毯子覆盖法计算多季相遥感影像每个像元的上分形信号和下分形信号,对比分析耕地和其他土地利用类型的分形特征,选取上分形信号的第3尺度作为特征尺度,提取商河县耕地空间分布特征;其次采用同时期的土地利用矢量数据、Esri land cover数据和统计数据进行耕地信息提取精度评价;最后分别设置多季相分形提取与单季相分形提取、现有土地利用数据产品的对比实验,并基于点位匹配度和面积匹配度进行评价。结果表明:多季相数据更能反映农作物生长的复杂性,有助于提高耕地信息的提取精度;不同土地利用类型在不同分形尺度的信号值各不相同,分形特征可以在不同尺度上清晰地刻画出不同土地利用类型的分异性;基于矢量数据和Esri land cover数据评价的多季相分形特征耕地提取点位匹配度为87.13%和89.83%,面积匹配度为99.73%和97.91%,均比单季相分形提取结果精度高;综合考虑点位匹配度、面积匹配度和空间分布特征,研发方法能有效区分耕地和其他土地利用类型,提取结果更优,且与统计数据有更高的一致性。该方法可准确提取耕地信息,为耕地的动态监测和损害评估提供技术支撑。

农牧交错区草地生物多样性研究进展:方法、挑战、不足与展望

土地利用变化是生物多样性丧失的主要驱动因素,农牧交错区耕地-草地界面变化对草地生物多样性有显著影响,系统梳理相关研究进展,对于区域可持续发展至关重要。本文首先从空间尺度、研究层次、研究方法和应用决策4个角度对农牧交错区草地生物多样性研究进展进行了系统阐述,然后分析了当前农牧交错区草地生物多样性保护面临的挑战和机遇,最后就目前已有研究的局限性和未来的发展方向进行了探讨。研究表明,当前农牧交错区“耕地-草地界面变化对生物多样性影响”的研究十分薄弱,研究层次有待扩展、跨尺度集成研究亟须加强。未来应当融入空天地一体化的遥感监测技术,系统推进农牧交错区草地生物多样性研究,在全面理解影响机制的基础上,探索耕地粮食生产和草地生物多样性保护的双赢方案,促进区域可持续发展。

我国羊养殖量及其粪尿资源化利用潜力分析

为了解我国各地区羊粪尿产量及其资源化利用潜力,本研究依据统计年鉴数据和粪尿产排污系数,估算了2021年我国各省份羊粪尿产生量、羊粪尿肥料化和能源化利用潜力,进一步测算了羊粪尿养分农田耕地负荷。结果表明,2021年全国羊粪尿总产生量达2.91亿t,其中总氮(TN)、总磷(TP)、总钾(TK)含量分别为91.08万、19.28万t和73.89万t,可有效替代氮肥(以尿素计)255.31万t、磷肥(以P_(2)O_(5)计)108.45万t、钾肥(以K_(2)O计)191.82万t,可产沼气165.95亿m^(3),具有较高的肥料化与能源化利用潜力。青海和西藏两省份农田耕地负荷居全国第一和第二,消纳羊粪尿时,应充分考虑耕地、农牧交错带、林地和草地等多种土地类型。

中国耕地种植制度遥感探测及其时空特征

[目的]耕地种植制度是农业生产方式的具体体现,其形成受自然资源要素与人类土地利用行为综合影响,反映了“人类-自然”的耦合关系。本研究旨在科学掌握全国耕地种植制度格局,为优化农业生产布局、提高农业生产能力、推动农业可持续性发展提供依据。[方法]结合遥感监测与空间决策树模型等手段,构建适合我国农情的跨年度种植制度探测方法体系,并开展空间格局分析。首先,通过辨析种植强度、复种指数等概念,从长期性、周期性、稳定性等方面,定义种植制度的内涵;其次,构建连续度、频度指标,并利用基于时序遥感的2001—2018年中国复种指数监测结果,结合时间滑动窗口方法,在像元尺度分别计算两个指标的具体值;最后,评估耕地的种植强度与种植制度特征的显著性,利用决策树方法确定种植制度类型,从区域差异、动态规律等方面分析不同区域种植制度的时空异质特征。[结果](1)面积上看,一年一熟所占面积最大,占53.52%,超过耕地总面积的一半;其次是一年两熟,占23.28%,季节性休耕(如两年三熟)与年度休耕分别占12.80%和6.94%。(2)空间上看,一年一熟、一年两熟、季节性休耕与年度休耕的集中分布区分别为东北地区、华北地区、长江以南地区与“镰刀弯”地区。(3)时间上看,动态稳定的种植制度从时间维度上揭示了静态复种指数背后的异质性,例如,2018年复种指数为1的区域,其中75.18%属于一年一熟、6.60%属于一年两熟、8.92%属于季节性休耕、8.02%属于年度休耕。[结论]本研究提出了一种结合时序遥感监测与空间决策树模型的跨年度分类体系,揭示了中国耕地种植制度分区聚集、种植强度南高北低的空间格局,直观展现了松嫩平原、“镰刀弯”等空间聚集区;分析了耕地复种与种植制度的时空差异特征,主要表现在种植制度与年度复种指数的空间不一致性,以及种植制度特有的周期性。研究结果可为合理提高耕地复种强度、推动实施“藏粮于地”战略提供案例支撑。