A regression tree-based method for integrating land-cover and land-use data collected at multiple scales

As data sets of multiple types and scales proliferate, it will be increasingly important to be able to flexibly combine them in ways that retain relevant information. A case in point is Amazonia, a large, data-poor region where most whole-basin data sets are limited to understanding land cover interpreted through a variety of remote sensing techniques and sensors. A growing body of work, however, indicates that the future state of much of Amazonia depends on the land use to which converted areas are put, but land use in the tropics is difficult to assess from remotely sensed data alone. An earlier paper developed new snapshots of agricultural land use in this region using a statistical fusion of satellite data and agricultural census data, an underutilized ancillary data source available across Amazonia. The creation of these land-use maps, which have the spatial detail of a satellite image and the attribute information of an agricultural census, required the development of a new statistical technique for merging data sets at different scales and of fundamentally different data types. Here we describe and assess this nonlinear technique, which reinterprets existing land cover classifications by determining what categories are most highly related to the polygon land-use data across the study area. Although developed for this region, the technique appears to hold broad promise for the systematic fusion of multiple data sets that are closely related but of different origins. The figures in the printed version of this article appear in black and white. Color figures are available from the author upon request.     

Effect of Widespread Agricultural Chemical Use on Butterfly Diversity across Turkish Provinces

Although agricultural intensification is thought to pose a significant threat to species, little is known about its role in driving biodiversity loss at regional scales. I assessed the effects of a major component of agricultural intensification, agricultural chemical use, and land‐cover and climatic variables on butterfly diversity across 81 provinces in Turkey, where agriculture is practiced extensively but with varying degrees of intensity. I determined butterfly species presence in each province from data on known butterfly distributions and calculated agricultural chemical use as the proportion of agricultural households that use chemical fertilizers and pesticides. I used constrained correspondence analyses and regression‐based multimodel inference to determine the effect of environmental variables on species composition and richness, respectively. The variation in butterfly species composition across the provinces was largely explained (78%) by the combination of agricultural chemical use, particularly pesticides, and climatic and land‐cover variables. Although overall butterfly richness was primarily explained by climatic and land‐cover variables, such as the area of natural vegetation cover, threatened butterfly richness and the relative number of threatened butterfly species decreased substantially as the proportion of agricultural households using pesticides increased. These findings suggest that widespread use of agricultural chemicals, or other components of agricultural intensification that may be collinear with pesticide use, pose an imminent threat to the biodiversity of Turkey. Accordingly, policies that mitigate agricultural intensification and promote low‐input farming practices are crucial for protecting threatened species from extinction in rapidly industrializing nations such as Turkey. Efectos del Uso Extensivo de Agroquímicos sobre la Diversidad de Mariposas en Provincias Turcas     

Risk-based modelling of diffuse land use impacts from rural landscapes upon salmonid fry abundance

Research has demonstrated that landscape or watershed scale processes can influence instream aquatic ecosystems, in terms of the impacts of delivery of fine sediment, solutes and organic matter. Testing such impacts upon populations of organisms (i.e. at the catchment scale) has not proven straightforward and differences have emerged in the conclusions reached. This is: (1) partly because different studies have focused upon different scales of enquiry; but also (2) because the emphasis upon upstream land cover has rarely addressed the extent to which such land covers are hydrologically connected, and hence able to deliver diffuse pollution, to the drainage network. However, there is a third issue. In order to develop suitable hydrological models, we need to conceptualise the process cascade. To do this, we need to know what matters to the organism being impacted by the hydrological system, such that we can identify which processes need to be modelled. Acquiring such knowledge is not easy, especially for organisms like fish that might occupy very different locations in the river over relatively short periods of time. However, and inevitably, hydrological modellers have started by building up piecemeal the aspects of the problem that we think matter to fish. Herein, we report two developments: (a) for the case of sediment associated diffuse pollution from agriculture, a risk-based modelling framework, SCIMAP, has been developed, which is distinct because it has an explicit focus upon hydrological connectivity; and (b) we use spatially distributed ecological data to infer the processes and the associated process parameters that matter to salmonid fry. We apply the model to spatially distributed salmon and fry data from the River Eden, Cumbria, England. The analysis shows, quite surprisingly, that arable land covers are relatively unimportant as drivers of fry abundance. What matters most is intensive pasture, a land cover that could be associated with a number of stressors on salmonid fry (e.g. pesticides, fine sediment) and which allows us to identify a series of risky field locations, where this land cover is readily connected to the river system by overland flow.     

Integrated biogeochemical modelling of nitrogen load from anthropogenic and natural sources in Japan

This study proposed an integrated biogeochemical modelling of nitrogen loads from anthropogenic and natural sources in Japan. Firstly, the nitrogen load (NL) from different sources such as crop, livestock, industrial plant, urban and rural resident was calculated by using datasets of fertilizer utilization, population distribution, land use map, and social census. Then, the nitrate leaching from soil layers in farmland, grassland and natural conditions was calculated by using a terrestrial nitrogen cycle model (TNCM). The Total Runoff Integrating Pathways (TRIP) was used to transport nitrogen from natural and anthropogenic sources through river channels, as well as collect and route nitrogen to the river mouths. The forcing meteorological and hydrological data is a 30-year (1976–2005) dataset for Japan which were obtained by the land surface model, Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO). For the model validation, we collected total nitrogen (TN) concentration data from 59 rivers in Japan. As a comparison result, calculated TN concentration values were in good agreement with the observed ones, which shows the reliability of the proposed model. Finally, the TN loads from point and nonpoint sources were summarized and evaluated for 59 river basins in Japan. The proposed modelling framework can be used as a tool for quantitative evaluation of nitrogen load in terrestrial ecosystems at a national scale. The connection to land use and climate data provides a possibility to use this model for analysis of climate change and land use change impacts on hydrology and water quality.     

15N-Nitrate signature in low-order streams: effects of land cover and agricultural practices.

Many studies have shown that intensive agricultural practices significantly increase the nitrogen concentration of stream surface waters, but it remains difficult to identify, quantify, and differentiate between terrestrial and in-stream sources or sinks of nitrogen, and rates of transformation. In this study we used the delta15N-NO3 signature in a watershed dominated by agriculture as an integrating marker to trace (1) the effects of the land cover and agricultural practices on stream-water N concentration in the upstream area of the hydrographic network, (2) influence of the in-stream processes on the NO3-N loads at the reach scale (100 m and 1000 m long), and (3) changes in delta15N-NO3 signature with increasing stream order (from first to third order). This study suggests that land cover and fertilization practices were the major determinants of delta15N-NO3 signature in first-order streams. NO3-N loads and delta15N-NO3 signature increased with fertilization intensity. Small changes in delta15N-NO3 signature and minor inputs of groundwater were observed along both types of reaches, suggesting the NO3-N load was slightly influenced by in-stream processes. The variability of NO3-N concentrations and delta15N signature decreased with increasing stream order, and the delta15N signature was positively correlated with watershed areas devoted to crops, supporting a dominant effect of agriculture compared to the effect of in-stream N processing. Consequently, land cover and fertilization practices are integrated in the natural isotopic signal at the third-order stream scale. The GIS analysis of the land cover coupled with natural-abundance isotope signature (delta15N) represents a potential tool to evaluate the effects of agricultural practices in rural catchments and the consequences of future changes in management policies at the regional scale.     

辽西大凌河流域土地利用变化及驱动力分析

从政策、流域综合治理、经济发展和技术进步、农民认知态度等4方面对影响大凌河流域土地利用变化的驱动力进行了分析。同时运用农户问卷调查和驱动力分析结果,选取影响耕地变化的社会经济和人口因子,运用主成分分析和多元迭代回归分析确定影响耕地变化的主要因子,并拟合出耕地变化的最优度模型。研究结果表明:在1987—2002年期间,农田和未利用荒地面积在不断减小,而林地、果园、草地在不断增加,但1995年后变化边际度大大减小;主成分分析表明影响土地利用变化主要影响因子是农业人口(A-POP)、总人口(T-POP)、农村经济收入(TIRE)、农林牧渔收入(IAFAF)和第三产业总产值(GTI);多元迭代回归分析表明耕地面积变化的最优回归模型中主变量是农业人口(A-POP)、总人口(T-POP)、农村经济收入(TIRE),这些变量能够解释95.1%的耕地变化。     

Projecting landscape changes in southern Chile: Simulation of human and natural processes driving land transformation

We describe a simulation model representing the most important human and natural factors driving land use and cover changes (LUCC) in southern Chile. We evaluate the model by examining its ability to simulate LUCC observed over the past three decades, conduct a sensitivity analysis of simulated trends to changes in important model parameters, and use the model to project likely landscape transformations over the next decade under “as usual,” “pessimistic,” and four “optimistic” scenarios. The model consists of five submodels representing LUCC on two distinct soil formations (volcanic ash and gleysols) and four major land use categories: native forest, agricultural land, shrubland, and urban land. Land use and cover sub-categories include old growth forests, secondary forests, and low and flooded shrubland. The model simulated well general historic trends in forest cover, agricultural land, shrubland, and urban land: from a forest-dominated landscape in 1976 to a landscape dominated by shrubland and agricultural land by 2007. Forest loss, forest degradation by logging and clearing for agriculture were the most important direct drivers of LUCC: forest logging and clearing were most important from 1976 to 1985, whereas after 1985 logging for firewood, driven by population growth, was most important. Sensitivity analysis indicated that model projections of general trends in the main land use and cover categories were not overly sensitive to changes in important model parameters, although further study is necessary to improve our estimates of the proportion of pasture requirements supplied by clearing low shrubland. Projections of LUCC suggested that a reduced amount of secondary forest would be left by 2017 if no actions are taken to reduce forest loss (“as usual”). Increasing population (“pessimistic scenario”) resulted in similar trajectories than those predicted by the as usual scenario, whereas reducing logging for firewood and increasing forest recruitment from shrubland could reduce loss of native forest by nearly one-third (“optimistic scenarios”). Surprisingly, shrubland exhibited the most complex and influential dynamics in all scenarios, being the immediate outcome of forest loss and the main long-term source of land for agriculture, urban expansion, and forest recovery. Few studies in Chile, or elsewhere, have considered the importance of this intermediate successional stage. Of the scenarios simulated, financial incentives targeted toward channeling shrubland into regenerated forest seemed most promising, although obstacles to such a management strategy exist.     

Composite likelihood approach to the regression analysis of spatial multivariate ordinal data and spatial compositional data with exact zero values

In many environmental and ecological studies, it is of interest to model compositional data. One approach is to consider positive random vectors that are subject to a unit-sum constraint. In landscape ecological studies, it is common that compositional data are also sampled in space with some elements of the composition absent at certain sampling sites. In this paper, we first propose a practical spatial multivariate ordered probit model for multivariate ordinal data, where the response variables can be viewed as the discretized non-negative compositions without the unit-sum constraint. We then propose a novel two-stage spatial mixture Dirichlet regression model. The first stage models the spatial dependence and the presence of exact zero values, and the second stage models all the non-zero compositional data. A maximum composite likelihood approach is developed for parameter estimation and inference in both the spatial multivariate ordered probit model and the two-stage spatial mixture Dirichlet regression model. The standard errors of the parameter estimates are computed by an estimate of the Godambe information matrix. A simulation study is conducted to evaluate the performance of the proposed models and methods. A land cover data example in landscape ecology further illustrates that accounting for spatial dependence can improve the accuracy in the prediction of presence/absence of different land covers as well as the magnitude of land cover compositions.     

Farmland size,chemical fertilizers,and irrigation management effects on maize and wheat yield in Mexico

ABSTRACT

Farmland size is a key factor in debates over agricultural land use, food security, agrochemical pollution, and the future of smallholder systems. This paper examines relationships between farmland size, chemical fertilizers and irrigation management, and maize and wheat yield in Mexico. We used agricultural census data to estimate the mean farmland areas and crop yields of 5.5 million farms and nine million agricultural plots in 2,455 Mexican municipalities. We also derived indices of socio-environmental and management factors to examine relationships with yield. Using multiple regression models, we found that although mean farmland area positively relates to maize and wheat yield, the relationships depend critically on the management contexts of chemical fertilizers and irrigation, which vary widely across farm size gradients. Smallholder yield gaps were associated with deficits in irrigation, rather than chemical inputs. Findings highlight the growing need for expanded irrigation access and/or water management assistance for smaller farms.     

三高农业与土肥

三高农业与土壤和肥料有密切的关系。地力是作物产量的重要基础,要取得高产,就必须坚持不懈地培肥地力。综合地力、土壤中营养元素及有害或有毒物质的含量、肥料类型和施肥技术,都对农产品的品质有很大的影响。要提高农产品的产量和质量,就要因土种植,合理施肥。高产优质是经济效益的主要构成因素,因而,培肥地力、因土种植、合理施肥能提高经济效益。忽视土肥工作,将影响三高农业的发展。     

平原城市河流面源污染研究范围及方法的选择与效果比较

由于平原城市地貌起伏不大,较难划分城市河流的流域范围,使得与河道面源污染相关的研究范围的边界确定存在一定的困难。针对这一问题,本文对比了基于缓冲区和排水区的两种研究范围及方法。根据遥感影像,利用GIS提取研究区内土地利用信息并与河流水质进行回归分析,对比两种范围及方法所得出的结果,试图为城市河道污染物的研究及管理提供方法支持和理论依据。本文选取了天津滨海地区11条河流进行研究,结果表明：（1）选取城市排水区作为土地利用数据的方法能够与河流中污染物的发生,迁移的真实情况相符合,能够综合考虑不同的景观格局分布对于水质的影响,结合平原城市的特殊性反映土地利用与河流污染物之间的相关关系,在机理严谨性上优于使用缓冲区范围,在整体性上更为完整;（2）基于缓冲区的研究方法能够在一定程度上解释在地表径流作用下,河流近岸不同距离的土地利用类型对河流污染物的影响,如耕地对污染物的影响,河流近岸100 m缓冲区的联合显著性为0.848,300 m缓冲区的仅为0.165;（3）土地利用类型对河流中污染物类型的贡献和消减趋势基本一致,城市居民点与工矿用地是河流当中重金属污染物如铅、汞的主要输入源;耕地对重金属等污染物有一定的截留消减作用,但在面积较大时增加了河流的有机污染物如氨氮;林地、园地、草地和水体对于城市河流污染物有一定的净化作用。两种方法结合起来可以得出更为详尽、客观的研究结果。     

Temporal-spatial dynamics of vegetation variation on non-point source nutrient pollution

The temporal-spatial interaction of land cover and non-point source (NPS) nutrient pollution were analyzed with the Soil and Water Assessment Tool (SWAT) to simulate the temporal-spatial features of NPS nutrient loading in the upper stream of the Yellow River catchment. The corresponding land cover data variance was expressed by the normalized difference vegetation index (NDVI) that was calculated from MODIS images. It was noted that the temporal variation of land cover NDVI was significantly correlated with NPS nutrient loading. The regression analysis indicated that vegetation not only detained NPS nutrient pollution transportation, but also contributed to sustainable loading. The temporal analysis also confirmed that regional NDVI was an effective index for monthly assessment of NPS nitrogen and phosphorus loading. The spatial variations of NPS nutrient loading can be classified with land cover status. The high loadings of NPS nitrogen in high NDVI subbasins indicated that forestry and farmland are the main critical loss areas. Farmland contributed sustainable soluble N, but the loading of soluble and organic N from grassland subbasins was much lower. Most P loading came from the areas covered with dense grassland and forestry, which cannot directly discharge to local water bodies. However, some NPS phosphorus from suburban farmland can directly discharge into adjacent water bodies. The interactions among nutrient loading, NDVI, and slope were also analyzed. This study confirmed that the integration of NPS modeling, geographic information systems, and remote sensing is needed to understand the interactive dynamics of NPS nutrient loading. Understanding the temporal-spatial variation of NPS nutrients and their correlations with land cover will help NPS pollution prevention and water quality management efforts. Therefore, the proposed method for evaluating NPS nutrient loading by land cover NDVI can be an effective tool for pollution evaluation and watersheds planning.     

Applying the Patuxent Landscape Unit Model to human dominated ecosystems: the case of agriculture

Non-spatial dynamics are core to landscape simulations. Unit models simulate system interactions aggregated within one space unit of resolution used within a spatial model. For unit models to be applicable to spatial simulations they have to be formulated in a general enough way to simulate all habitat elements within the landscape. Within the Patuxent River watershed, human dominated land uses, such as agriculture and urban land, are already 50% of the current land use, while urban land is replacing forests, agriculture and wetlands at a rapid rate. The Patuxent Landscape Model (PLM) with the Patuxent General Unit Model as core (Pat-GEM) was developed as a predictive policy tool to estimate environmental impacts of such land use changes. The Pat-GEM is based on the General Ecosystem Model (GEM) developed by [Ecol. Modelling 88 1996 263]. Previous calibrations of the Pat-GEM for anthropogenic land uses have not been satisfactory due to the scarcity of appropriate data. This paper shows Pat-GEM simulations of biomass growth and nutrient uptake for crops typical within the Patuxent watershed. The Pat-GEM was expanded to include processes and fluxes that characterize agricultural land use. The most important extension was to include crop rotation into the model. Additionally, we refined the processes for planting, harvesting and fertilization by introducing specific growth parameters. Our revised Pat-GEM was calibrated against the results from Erosion Productivity Impact Calculator (EPIC) a widely used and calibrated agricultural model. We achieved high correlation between results generated with Pat-GEM and EPIC. The correlation coefficients (r²) varied between 0.87 and 0.98, with the simulation results for winter wheat showing the lowest correlation coefficients. Intercalibration using EPIC is a powerful method for calibrating the Pat-GEM model for agricultural land use. EPIC was able (a) to provide about 30% of the input data required for running the Pat-GEM model; and (b) to provide time series output data (with a daily time step) to calibrate the output variables biomass production and nutrient uptake.     

Model of Japanese serow (Capricornis crispus) energetics predicts distribution on Honshu, Japan.

Understanding what determines a species' range is a central objective in ecology and evolutionary biology. It has important applications for predicting species distributions and how they might respond to environmental perturbations. This paper describes a mechanistic approach to predict past and present distribution of the Japanese serow (Capricornis crispus) on Honshu, Japan. We applied state-of-the-art microclimate and animal biophysical/behavioral models coupled with climate and vegetation data to estimate the distribution of potential range expansion under protection. We tested the model results against detailed empirical distribution data from the Ministry of the Environment for a five-prefecture area in central Honshu. We also applied the models to time-series land use/cover maps to investigate the historical transitions in habitat suitability during 1947-1999 in the Arai-Keinan region. This is the first time to our knowledge that mechanistic models have successfully predicted the landscape scale distribution of a mammal species in the absence of other animal species interactions, such as predators. In this case, animal energetics/behavior-plant interactions seem to be critical. Forest cover appears to be important in summer and winter for suitable serow habitats. The energetics model results indicate that the serow can overheat in some open environments in midday hours in summer. In winter, simulation results suggested that forest cover provides effective refuge to avoid increased metabolic demands of cold temperatures and strong winds. The model simulations suggested that land use/cover changes documented during 1947-1999 resulted in increased suitable serow habitat due to expanding forest cover from agricultural marginalization and ecological succession. The models provide a unique tool for estimating species' range expansion under protection or for selecting suitable reintroduction sites.     

The alignment of agricultural and nature conservation policies in the European Union

Europe is a region of relatively high population density and productive agriculture subject to substantial government intervention under the Common Agricultural Policy (CAP). Many habitats and species of high conservation interest have been created by the maintenance of agricultural practices over long periods. These practices are often no longer profitable, and nature conservation initiatives require government support to cover the cost for them to be continued. The CAP has been reformed both to reduce production of agricultural commodities at costs in excess of world prices and to establish incentives for landholders to adopt voluntary conservation measures. A separate nature conservation policy has established an extensive series of protected sites (Natura 2000) that has, as yet, failed to halt the loss of biodiversity. Additional broader scale approaches have been advocated for conservation in the wider landscape matrix, including the alignment of agricultural and nature conservation policies, which remains a challenge. Possibilities for alignment include further shifting of funds from general support for farmers toward targeted payments for biodiversity goals at larger scales and adoption of an ecosystem approach. The European response to the competing demands for land resources may offer lessons globally as demands on rural land increase.     

Land cover types and ecological conditions of the Estonian coast

Detailed analysis of the land cover of the Estonian coastal zone is presented based on Estonian laws on coastal zone management, the CORINE Land Cover (CLC) system, the status of protected areas, and administrative division data of Estonia. By law the coastal zone is defined as a 200-m wide zone landward from the mean sea level line. The length of the Estonian coastline (including the islands) is 3794 km. The 200-m zone of the Estonian coast is very diverse. Out of the 34 CORINE land cover types represented in Estonia 30 are found in the coastal zone. Three dominating land cover types in the coastal zone of Estonia are inland marshes, coniferous forest and semi-natural grassland. Their total share is 47%; the other 27 land cover types represented here cover 53% of the coastal zone. The Estonian coastal zone is generally in a good natural condition. The proportion of artificial surfaces throughout the zone is merely 4.7%, while agricultural landscapes cover only ca. 10%. Land cover data for the coastal zone are also presented by county. Of the 200-m coastal zone 24% is under protection, which is more than twice the value for Estonia as a whole (11%). Legislative protection of the coastal zone is presently satisfactory. The use of the CORINE Land Cover system enables comparisons with other European regions since CLC data have been compiled for most of Europe.     

Deforestation and Development in a Small Open Economy

Our economy faces world prices and starts with a large endowment of land in forest and a small endowment of land in agriculture. Clearing of forested land yields marketable timber and a unit of land for agriculture. Early, the high price of agricultural land drives the clearing process. Later, the profit from marketing timber from the cleared land drives the process. Dates of “phase transitions” are endogenous. We also set out a submodel of perpetual cyclic clearing and reforesting, and show that it when cyclical harvesting—regrowing is optimal, the extent of clearing can be relatively large.     

Satellite detection of bird communities in tropical countryside.

The future of biodiversity hinges partly on realizing the potentially high conservation value of human-dominated countryside. The characteristics of the countryside that promote biodiversity preservation remain poorly understood, however, particularly at the fine scales at which individual farmers tend to make land use decisions. To address this problem, we explored the use of a rapid remote sensing method for estimating bird community composition in tropical countryside, using a two-step process. First, we asked how fine-grained variation in land cover affected community composition. Second, we determined whether the observed changes in community composition correlated with three easily accessible remote sensing metrics (wetness, greenness, and brightness), derived from performing a tasseled-cap transformation on a Landsat Enhanced Thematic Mapper Plus image. As a comparison, we also examined whether the most commonly used remote sensing indicator in ecology, the Normalized Difference Vegetation Index (NDVI), correlated with community composition. We worked within an agricultural landscape in southern Costa Rica, where the land comprised a complex and highly heterogeneous mosaic of remnant native vegetation, pasture, coffee cultivation, and other crops. In this region, we selected 12 study sites (each < 60 ha) that encompassed the range of available land cover possibilities in the countryside. Within each site, we surveyed bird communities within all major land cover types, and we conducted detailed field mapping of land cover. We found that the number of forest-affiliated species increased with forest cover and decreased with residential area across sites. Conversely, the number of agriculture-affiliated species using forest increased with land area devoted to agricultural and residential uses. Interestingly, we found that the wetness and brightness metrics predicted the number of forest- and agriculture-affiliated species within a site as well as did detailed field-generated maps of land cover. In contrast, NDVI and the closely correlated greenness metric did not correlate with land cover or with bird communities. Our study shows the strong potential of the tasseled-cap transformation as a tool for assessing the conservation value of countryside for biodiversity.     

Modeling Opportunity Costs of Conservation in Transitional Landscapes

Abstract:  Conservation scientists recognize the urgency of incorporating opportunity costs into conservation planning. Despite this, applications to date have been limited, perhaps partly because of the difficulty in determining costs in regions with limited data on land prices and ownership. We present methods for estimating opportunity costs of land preservation in landscapes or ecoregions that are a changing mix of agriculture and natural habitat. Our approach derives from the literature on estimating land values as opportunity costs of alternate land uses and takes advantage of general availability of necessary data, even in relatively data-poor regions. The methods integrate probabilities of habitat conversion with region-wide estimates of economic benefits from agricultural land uses and estimate land values with a discount rate to convert annual values into net present values. We applied our method in a landscape undergoing agricultural conversion in Paraguay. Our model of opportunity costs predicted an independent data set of land values and was consistent with implicit discount rates of 15–25%. Model-generated land values were strongly correlated with actual land values even after correcting for the effect of property size and proportion of property that was forested. We used the model to produce a map of opportunity costs and to estimate the costs of conserving forest within two proposed corridors in the landscape. This method can be applied to conservation planning in situations where natural habitat is currently being converted to market-oriented land uses. Incorporating not only biological attributes but also socioeconomic data can help in the design of efficient networks of protected areas that represent biodiversity at minimum costs.     

土壤学与广东持续农业的发展

持续农业是关于建立资源、生态、地力、经济效益等各个方面均可持续发展的农业制度的一种思想．水土流失、土地退化、耕地锐减、环境污染，制约着广东农业的持续发展．土壤学应围绕持续农业开展研究工作．目前应加强土壤生态环境的保护，防治土地退化，提高土壤肥力，保证耕地生产力的持续发展．应开展土壤资源的动态变化研究，为合理开发和利用土壤资源提供科学依据．