Cropland Change Research Articles

Agroforestry systems: Meta‐analysis of soil carbon stocks, sequestration processes, and future potentials

AbstractAgroforestry (AF) has the potential to restore degraded lands, provide a broader range of ecosystem goods and services such as carbon (C) sequestration and high biodiversity, and increase soil fertility and ecosystem stability through additional C input from trees, erosion prevention, and microclimate improvement. Advantages and processes for global C sequestration in AF are unknown. We used a meta‐analysis of 427 soil C stock data pairs grouped into four main AF systems—alley cropping, windbreaks, silvopastures, and homegardens—and evaluated changes in AF and adjacent control cropland or pasture. Mean soil C stocks in AF (1‐m depth) were 126 Mg C·ha−1, which is 19% more than that in cropland or pasture. The highest C stocks in soil were in subtropical homegardens, AF with younger trees, and topsoil (0–20 cm). Increased soil C stocks in AF were lower than aboveground C stocks in most AF systems, except alley cropping. Homegardens stored the highest C in both aboveground and belowground, especially in the subsoil (20–100 cm). Advantages of AF ecosystem services focusing on mechanisms of belowground C sequestration were analyzed. AF could store 5.3 × 109 Mg additional C in soil on 944 Mha globally, with most in the tropics and subtropics. AF systems could greatly contribute to global soil C sequestration if used in larger areas. Future investigations of AF should include (a) mechanistic‐ and process‐based studies (instead of common monitoring and inventories), (b) models linking forest and crop growth with soil water and C and nutrient cycling, and (c) accurate assessments of the AF area worldwide based on the remote sensing approaches.

The Impact of Agricultural Land Use Change on Lake Water Quality: Evidence from Iowa

The environmental impacts of agricultural policies must be quantified to perform full cost-benefit analyses and make informed policy decisions. In this paper I use a unique panel data set to estimate the effect of changes in cropland on lake water quality. Fifteen years of water quality measurements across over 100 lakes are combined with satellite imagery and weather data. Using a dynamic panel data model, I find that the elasticity of water quality to cropland is 0.0535. To understand the policy implications, I estimate a second model to find the elasticity of cropland to crop prices. I combine these estimates to analyze the effect of the Renewable Fuel Standard (RFS). I find that the RFS decreased lake water quality; however, the magnitude of this effect is negligible.

The Impact of Agricultural Land Use Change on Lake Water Quality: Evidence from Iowa

The environmental impacts of agricultural policies must be quantified to perform full cost-benefit analyses and make informed policy decisions. In this paper I use a unique panel data set to estimate the effect of changes in cropland on lake water quality. Fifteen years of water quality measurements across over 100 lakes are combined with satellite imagery and weather data. Using a dynamic panel data model, I find that the elasticity of water quality to cropland is 0.0535. To understand the policy implications, I estimate a second model to find the elasticity of cropland to crop prices. I combine these estimates to analyze the effect of the Renewable Fuel Standard (RFS). I find that the RFS decreased lake water quality; however, the magnitude of this effect is negligible.

Impacts of socioeconomic factors on cropland transition and its adaptation in Beijing, China

Over the past decades, croplands in China experienced rapid losses as a result of nationwide urbanization, industrialization, socioeconomic development, and implementation of ecological projects. These developments were driven by continuous economic reforms and environmental protection policies. Croplands are precious land resources influenced by overpopulation in China. Cropland losses threaten national food security. This study analyzed spatial patterns of cropland changes, driving forces, and its adaptation. Object-based approach was adopted to accurately measure cropland changes using remote sensing data. Conversion matrix and statistical regression model were used to explore impacts of socioeconomic factors on cropland changes. A case study on Beijing revealed that cropland in Beijing decreased by 47% in the past 35 years. Primary driving forces in cropland loss included urban expansion, implementation of ecological projects, orchard plantation, and water resource protection. Increased migration and economic development resulted in urban expansion. To promote sustainable development and to meet demands of urban and environmental protection, cropland adaptation was performed by increasing value-added commercial crop plantation, improving crop yield, and reducing cultivation intensity for effective water consumption.

Determination of land salinization causes via land cover and hydrological process change detection in a typical part of Songnen Plain

Causes of land salinization were determined via land cover and hydrological process change detection in a typical part of Songnen Plain. The area of saline land increased from 4627 km2 in 1980 to 5416 km2 in 2000, and then decreased to 5198 km2 in 2015. The transformation between saline land and other land covers happened mainly before 2000, and saline land had transformation relationship mainly with cropland, grassland, and water body. From 1979 to 2007, groundwater depth fluctuated to increase and was mainly deeper than 3.3 m. Spatially, the area of the region where groundwater depth was deeper than 3.3 m increased from 46.7% in 1980 to 84% in 2000, while the area of the region almost occupied the whole region after 2000. Precipitation and evaporation changed little, while runoff decreased substantially. Shallow groundwater, change of cropland, grassland, and water body induced from human activities and decrease of runoff and increase of irrigation and water transfer from outer basin were the main reasons for land salinization before 2000. After 2000, groundwater with relatively great depth could not exert great influence on land salinization. Protection of grassland and wetland prevented the increase of the area of saline land.

Comparative Study on Changes of Croplands Between North Korea and South Korea During 1990–2015

Studies on long-term change of cropland is of great significance to the utilization of land resources and the implementation of scientific agricultural policies. The Korean Peninsula, adjacent to China, plays an important role in the international environment of Northeast Asia. The Korean Peninsula includes South Korea and North Korea—two countries that have a great difference in their institutions and economic developments. Therefore, we aim to quantify the spatiotemporal changes of croplands in these two countries using Landsat Thematic Imager (TM) and Operational Land Imager (OLI) imagery, and to compare the differences of cropland changes between the two countries. This paper take full advantage of ODM approach (object-oriented segmentation and decision-tree classification based on multi-season imageries) to obtain the distribution of croplands in 1990 and 2015. Results showed that the overall classification accuracy of cropland data is 91.10% in 1990 and 92.52% in 2015. The croplands were mainly distributed in areas with slopes that were less than 8° and with elevations that were less than 300 m in the Korean Peninsula. However, in other region (slope > 8° or elevation > 300 m), the area and proportion of North Korea’s croplands were significantly higher than that of South Korea. Croplands significantly increased by 15.02% in North Korea from 1990 to 2015. In contrast, croplands in South Korea slightly decreased by 1.32%. During the 25 years, policy shift, economic development, population growth, and urban sprawl played primary roles for cropland changes. Additionally, the regional differences of cropland changes were mainly due to different agriculture policies implemented by different countries. The achievements of this study can provide scientific guidance for the protection and sustainability of land resources.

How much CO2 at 1.5 °C and 2 °C?

The atmospheric concentration of CO2 at the time of passing 1.5 °C or 2 °C is unknown due to uncertainties in climate sensitivity and the concentrations of other GHGs. Impacts studies must account for a wide range of concentrations to avoid either over- or underestimating changes in crop yields and land and marine biodiversity.

Detection of Cropland Change Using Multi-Harmonic Based Phenological Trajectory Similarity

Accurate information on cropland changes is critical for food production and security, sustainable cropland management, and global change studies. The common change detection methods bi-temporal based, using remotely sensed imagery easily generate pseudo changes due to phenological or seasonal differences. Cropland exhibits a distinctive phenological trajectory that has strong periodic characteristics and seasonal paths. This paper proposes the use of phenological trajectory similarity to search for the overall changes between two time-series images instead of single change events between two dates of imagery. Due to the complex spectral–temporal characteristic of cropland, a phenological trajectory was constructed using a multi-harmonic model for capturing intra-annual variations. Then, phenological trajectory similarity was measured using coefficient vector difference (CVD), and used for detecting change/no-change areas when considering both the amplitude and phase difference. Finally, instead of the traditional classification method based on original images, we used the coefficient ratio vector (CRV) as the input for change type discrimination. The distance between the coefficient ratio vector (CRV) of the change pixel and of the reference change type was calculated to identify the exactly changed types. The performance of this proposed approach was tested using two sets of Landsat time-series images from 2010 and 2015. Moreover, the change area detection results of three other methods, namely, the continuous change detection and classification (CCDC), change vector analysis (CVA), and post-classification comparison (PCC), were also calculated for comparison and analysis. The results indicated that the proposed approach acquired the highest accuracy with an overall accuracy of 98.58% and a kappa coefficient of 0.82, which demonstrated that the method provides the capacity to detect real changes and estimate pseudo changes caused by season differences.

Trend forecast based approach for cropland change detection using Lansat-derived time-series metrics

ABSTRACTAccurate information on cropland changes is critical for understanding greenhouse gas emissions, biodiversity, food safety, and human welfare. Traditional bi-temporal change detection methods using remotely sensed imagery may generate pseudochanges due to phenological differences and interference factors. In this study, we develop the Trend Forecast-based change detection approach (TFCD) using Landsat-derived time-series metrics to eliminate pseudochanges caused by phenological differences. Assuming that time-series images could be modelled and analysed, the time-series model would have a high capacity for revealing trends and temporal patterns. The spectral variability of cropland has strong seasonal dynamics, which shows short-period regular changes and long-term dynamic trends. Therefore, multi-harmonic model is used to describe the trend and temporal patterns of cropland over time. Then, the differences between model predicted and observed trajectory are used to detect the change areas. Finally, the change types are determined using the model coefficients. The effectiveness of this method was verified using a stack of (25 images) Landsat Enhanced Thematic Mapper Plus and Operational Land Imager images from two years (2014 and 2015). The results indicated that TFCD correctly detected true changes, with 95.79% overall accuracy and a Kappa coefficient of 0.751, and that the method was superior to the traditional methods.

Post‐socialist cropland changes and abandonment in Mongolia

AbstractLarge‐scale cropland changes have significant implications for global and national food supply as well as degradation in land resources. We examined cropland dynamics at the national scale in Mongolia over the last three decades using Google Earth Engine cloud computing and 11,360 Landsat satellite images. Our overarching goal was to develop the first comprehensive, high‐resolution maps of Mongolia's cropland extent for two nominal years: 1990 at the peak of the socialist era production and 2014 after major socio‐political and economic regime changes. By comparing the two cropland maps, we estimate cropland changes and identify areas of post‐socialist cropland abandonment. Our cropland classifications produce high overall accuracies (overall weighted, statistical accuracies of 98% and 97%, respectively), and the cropland area estimates compare well with county‐level crop area statistics (R2 = 0.91, p value < .001). We estimate 38% cropland decline in Mongolia throughout the major crop regions but 18% increase in the central region due to increased concentration of crop production. The observed large‐scale cropland abandonment would be expected to result in substantial land conversion to rangelands as well as land degradation in the abandoned fields, including soil erosion, increased contribution to desertification, and reduced species diversity. Future studies can monitor the long‐term trajectory in the abandoned crop fields and recommend targeted restoration strategies.

Reducing Uncertainties in Applying Remotely Sensed Land Use and Land Cover Maps in Land-Atmosphere Interaction: Identifying Change in Space and Time

Land use and land cover (LULC) data are a central component of most land-atmosphere interaction studies, but there are two common and highly problematic scale mismatches between LULC and climate data. First, in the spatial domain, researchers rarely consider the impact of scaling up fine-scale LULC data to match coarse-scale climate datasets. Second, in the temporal domain, climate data typically have sub-daily, daily, monthly, or annual resolution, but LULC datasets often have much coarser (e.g., decadal) resolution. We first explored the effect of three spatial scaling methods on correlations among LULC data and a land surface climatic variable, latent heat flux in China. Scaling by a fractional method preserved significant correlations among LULC data and latent heat flux at all three studied scales (0.5°, 1.0°, and 2.5°), whereas nearest-neighbor and majority-aggregation methods caused these correlations to diminish and even become statistically non-significant at coarser spatial scales (i.e., 2.5°). In the temporal domain, we identified fractional changes in croplands, forests, and grasslands in China using a recently developed and annually resolved time series of LULC maps from 1982 to 2012. Relative to common LULC change (LULCC) analyses conducted over two-time steps or several time periods, this annually resolved, 31-year time series of LULC maps enables robust interpretation of LULCC. Specifically, the annual resolution of these data enabled us to more precisely observe three key and statistically significant LULCC trends and transitions that could have consequential effects on land-atmosphere interaction: (1) decreasing grasslands to increasing croplands in the Northeast China plain and the Yellow river basin, (2) decreasing croplands to increasing forests in the Yangtze river basin, and (3) decreasing grasslands to increasing forests in Southwest China. Our study not only demonstrates the importance of using a fractional spatial rescaling method, but also illustrates the value of annually resolved LULC time series for detecting significant trends and transitions in LULCC, thus potentially facilitating a more robust use of remotely sensed data in land-atmosphere interaction studies.

The Dynamics of Land Use/Cover and the Statistical Assessment of Cropland Change Drivers in the Kabul River Basin, Afghanistan

To cope with the growing agrarian crises in Afghanistan, the government (following the fall of the Taliban regime in 2002) has taken measures through cropland expansion “extensification” and switching to mechanized agriculture “intensification”. However, cropland expansion, on one hand, disturbs the existing land use/cover (LULC) and, on other hand, many socio-economic and biophysical factors affect this process. This study was based on the Kabul River Basin to answer two questions: Firstly, what was the change in LULC since 2001 to 2010 and, secondly, what are the drivers of cropland change. We used the spatial calculating model (SCM) for LULC change and binomial logistic regression (BLR) for drivers of cropland change. The net change shows that cropland, grassland, water-bodies, and built-up areas were increased, while forest, unused, and snow/ice areas were decreased. Cropland was expanded by 13%, which was positively affected by low and plain landforms, slope, soil depth, investment on agriculture and distance to the city, while it was negatively affected by plateaus and hill landforms, dry semi-arid, moist semi-arid, and sub-humid zones, precipitation, population, and the distance to roads and water. Climate adaptation measures, cropland protection in flood prone zones, population and rural migration control, farmer access to credit, irrigation, and inputs are necessary for agricultural deployment.

GlobeLand30 shows little cropland area loss but greater fragmentation in China

Understanding of cropland dynamics in a large geographical extent is mostly based on observations of area change, while the changes in landscape pattern are hardly assessed. The total amount of cropland in China has remained relatively stable in recent years, which might suggest there was little change. In this analysis, we combine the number of cropland patches (NP) with the total cropland area (TA) for a more comprehensive characterization of cropland change in China. We use GlobeLand30–a global land cover dataset with a 30m resolution for the years 2000 and 2010–and characterize changes in TA and NP for each county as increase, stable, or decrease. This characterization shows that 703 out of 2420 counties experienced both cropland loss and increased fragmentation. The predominant cropland loss in these areas, especially in the North China Plain, is converted to artificial land. Another 212 are characterized by the opposite developments: an increase in cropland and decreased fragmentation. These counties, are mainly characterized by a conversion of forest areas and grassland areas. It suggests that the cropland conservation policy in China effectively protected the total cropland area in overall, but the consequences in terms of fragmentation might be underestimated. Counties with no obvious change in both indicators, measuring 279 counties, are mainly located in the Southeast. Our results are further compared with local level case studies: the fair consistency indicates alternatives of applying GlobeLand30 for analyzing landscape changes across scales and for cross-site comparisons.

Monitoring Changes in Croplands Due to Water Stress in the Krishna River Basin Using Temporal Satellite Imagery

Remote sensing-based assessments of large river basins such as the Krishna, which supplies water to many states in India, are useful for operationally monitoring agriculture, especially basins that are affected by abiotic stress. Moderate-Resolution Imaging Spectroradiometer (MODIS) time series products can be used to understand cropland changes at the basin level due to abiotic stresses, especially water scarcity. Spectral matching techniques were used to identify land use/land cover (LULC) areas for two crop years: 2013–2014, which was a normal year, and 2015–2016, which was a water stress year. Water stress-affected crop areas were categorized into three classes—severe, moderate and mild—based on the normalized difference vegetation index (NDVI) and intensity of damage assessed through field sampling. Furthermore, ground survey data were used to assess the accuracy of MODIS-derived classification individual products. Water inflows into and outflows from the Krishna river basin during the study period were used as direct indicators of water scarcity/availability in the Krishna Basin. Furthermore, ground survey data were used to assess the accuracy of MODIS-derived LULC classification of individual year products. Rainfall data from the tropical rainfall monitoring mission (TRMM) was used to support the water stress analysis. The nine LULC classes derived using the MODIS temporal imagery provided overall accuracies of 82% for the cropping year 2013–2014 and 85% for the year 2015–2016. Kappa values are 0.78 for 2013–2014 and 0.82 for 2015–2016. MODIS-derived cropland areas were compared with national statistics for the cropping year 2013–2014 with a R2 value of 0.87. Results show that both rainfed and irrigated areas in 2015–2016 saw significant changes that will have significant impacts on food security. It has been also observed that the farmers in the basin tend to use lower inputs and labour per ha during drought years. Among all, access to water is the major driver determining the crop choice and extent of input-use in the basin.

Eco‐hydrological effects associated with environmental flow management: A case study from the arid desert region of China

AbstractIn arid regions, stream‐flow regulation impacts the hydrological processes and riparian ecosystems in both direct and indirect ways. Thus, understanding the degree of eco‐hydrological effects over different spatiotemporal scales is critical for effective stream‐flow regulation. This study investigates the multiple hydro‐ecological effects caused by a mandatory implementation of stream‐flow regulation in a typical endorheic river basin, the Heihe River Basin (HRB), in Northwest China. The changes in the stream‐flow regime, spatiotemporal patterns of vegetation and groundwater flow system were quantified using stream‐flow data, leaf area index products of 1 km resolution and a 3D numerical groundwater flow model. The results show that the stream leakage to groundwater is strongly correlated with the disturbance by stream‐flow regulation, moderately with the evaporation and inflow to terminal lake, but only weakly with the total vegetation state and cropland change. Even though the average groundwater level for the entire lower HRB has an upward trend in response to increasing stream flow, it has no significant correlation with flow regulation. The groundwater system acts as a “temporary bank” to buffer the regulated stream flow for the entire lower HRB. The spatiotemporal patterns of the leaf area index distribution point to the stream‐flow regulation as triggers for the increase in cropland and vegetation patches in the flat terrain along the river branches. This case study in the lower HRB has provided valuable database and insights for environmental flow management of other arid watersheds.

Nitrogen balance dynamics during 2000-2010 in the Yangtze River Basin croplands, with special reference to the relative contributions of cropland area and synthetic fertilizer N application rate changes.

With the increases of cropland area and fertilizer nitrogen (N) application rate, general N balance characteristics in regional agroecosystems have been widely documented. However, few studies have quantitatively analyzed the drivers of spatial changes in the N budget. We constructed a mass balance model of the N budget at the soil surface using a database of county-level agricultural statistics to analyze N input, output, and proportional contribution of various factors to the overall N input changes in croplands during 2000–2010 in the Yangtze River Basin, the largest basin and the main agricultural production region in China. Over the period investigated, N input increased by 9%. Of this 87% was from fertilizer N input. In the upper and middle reaches of the basin, the increased synthetic fertilizer N application rate accounted for 84% and 76% of the N input increase, respectively, mainly due to increased N input in the cropland that previously had low synthetic fertilizer N application rate. In lower reaches of the basin, mainly due to urbanization, the decrease in cropland area and synthetic fertilizer N application rate nearly equally contributed to decreases in N input. Quantifying spatial N inputs can provide critical managerial information needed to optimize synthetic fertilizer N application rate and monitor the impacts of urbanization on agricultural production, helping to decrease agricultural environment risk and maintain sustainable agricultural production in different areas.

SPATIAL PATTERN OF HYDROLOGIC RESPONSE UNIT (HRU) EFFECT ON FLOW DISCHARGE OF CI RASEA WATERSHED USING LANDSAT TM IN 1997 TO 2009

Hydrologic Response Unit (HRU) is a unit formed of hydrological analysis based on geology and soil type, slope, and land cover. This paper discussed the spatial pattern of Hydrologic Response Unit (HRU) in 1997-2009 and its impact on flow Ci Rasea watershed temporally. In this study, SWAT (Soil and Water Assessment Tool) model, based on land cover changed, was used to get HRU and flow in spatially and temporally. This method used Landsat TM 1997, 2003 and 2009 data for land cover and daily rainfall 1997-2009 for flow modeling. The results showed the spatial pattern of HRU in temporally was affected by landcover based on the changing of HRU. The majority of HRU spatial pattern at Ci Rasea watershed were clustered. During 1997-2009, accumulated surface runoff and the changing of flow discharge were affected by changes of HRU spatial pattern. The biggest accumulated surface runoff in Ci Rasea watershed influenced by HRU of agricultural cropland in area of clay soil type with slope slightly obliquely. While the smallest accumulated surface runoff in Ci Rasea watershed influenced by HRU of paddy field in the area of sandy loam soil type with a gentle slope. The changes of HRU agriculture cropland become HRU mixed cropland in area clay soil type with slope at a slight angle and HRU agriculture cropland become HRU paddy field in area, sandy loam soil type with a gentle slope could be decreasing the accumulation of surface runoff in Ci Rasea watershed.

Monitoring cropland changes along the Nile River in Egypt over past three decades (1984–2015) using remote sensing

ABSTRACTThe Nile River basin is the main agricultural area in Egypt. In recent decades, human activities and climate change have remarkably influenced the ecological environment there. Those changes have caused land degradation, sea level rise, and conflicts between land and population, threatening the agricultural system and food security of Egypt. In this study, cropland mapping along the Nile in Egypt over the past three decades (1984–2015) was conducted at annual frequency, using 961 Landsat TM/ETM+/OLI images. Spectral features of selected growing season images and band ratio-based indices were used in supervised classification. Thereafter, terrain and time series information were used to filter possible classification errors on the basis of logical judgment and statistical analysis. The average overall classification accuracy of cropland was greater than 90%. Furthermore, temporal and spatial characteristics of cropland expansion were analysed. The results highlight the annual geographical distribution of cropland dynamics from the Nile Valley to desert. In total, cropland areas had increased by 33.7% from 2848.1 kha in 1984 to 3807.8 kha in 2015, with an annual average increase of 31.0 kha in these 32 years.

Hydrological effects of cropland and climatic changes in arid and semi-arid river basins: A case study from the Yellow River basin, China

The Yellow River basin is a typical semi-arid river basin in northern China. Serious water shortages have negative impacts on regional socioeconomic development. Recent years have witnessed changes in streamflow processes due to increasing human activities, such as agricultural activities and construction of dams and water reservoirs, and climatic changes, e.g. precipitation and temperature. This study attempts to investigate factors potentially driving changes in different streamflow components defined by different quantiles. The data used were daily streamflow data for the 1959–2005 period from 5 hydrological stations, daily precipitation and temperature data from 77 meteorological stations and data pertaining to cropland and large reservoirs. Results indicate a general decrease in streamflow across the Yellow River basin. Moreover significant decreasing streamflow has been observed in the middle and lower Yellow River basin with change points during the mid-1980s till the mid-1990s. The changes of cropland affect the streamflow components and also the cumulative effects on streamflow variations. Recent years have witnessed moderate cropland variations which result in moderate streamflow changes. Further, precipitation also plays a critical role in changes of streamflow components and human activities, i.e. cropland changes, temperature changes and building of water reservoirs, tend to have increasing impacts on hydrological processes across the Yellow River basin. This study provides a theoretical framework for the study of the hydrological effects of human activities and climatic changes on basins over the globe.

Projecting regional climate and cropland changes using a linked biogeophysical‐socioeconomic modeling framework: 1. Model description and an equilibrium application over West Africa

AbstractAgricultural land use alters regional climate through modifying the surface mass, energy, and momentum fluxes; climate influences agricultural land use through their impact on crop yields. These interactions are not well understood and have not been adequately considered in climate projections. This study tackles the critical linkages within the coupled natural‐human system of West Africa in a changing climate based on an equilibrium application of a modeling framework that asynchronously couples models of regional climate, crop yield, multimarket agricultural economics, and cropland expansion. Using this regional modeling framework driven with two global climate models, we assess the contributions of land use change (LUC) and greenhouse gas (GHGs) concentration changes to regional climate changes and assess the contribution of climate change and socioeconomic factors to agricultural land use changes. For future cropland expansion in West Africa, our results suggest that socioeconomic development would be the dominant driver in the east (where current cropland coverage is already high) and climate changes would be the primary driver in the west (where future yield drop is severe). For future climate, it is found that agricultural expansion would cause a dry signal in the west and a wet signal in the east downwind, with an east‐west contrast similar to the GHG‐induced changes. Over a substantial portion of West Africa, the strength of the LUC‐induced climate signals is comparable to the GHG‐induced changes. Uncertainties originating from the driving global models are small; human decision making related to land use and international trade is a major source of uncertainty.