Cropland Net Primary Production Research Articles

Assessment the impact of urban expansion on cropland net primary productivity in Northeast China

Urban development inevitably comes with the loss of prime agricultural land, leading to a decline in cropland productivity. The Northeast region is an important grain-producing region in China, and agricultural land is of great significance in ensuring national food security. Here, land use and cover data and MODIS-NPP data were used to comprehensively understand the direct and adverse influence of urban land growth on cropland net primary productivity (NPP) in Northeast China. The results indicated that (1) in Northeast China, the urban land area increased by a total of 5230.00 km2 since 2000. The urban land growth phenomenon was particularly notable in the cities near the central transportation trunk line and along the southern coastal regions. (2) more than half of the newly added urban land patches originated from cropland. Between 2000 and 2020, urban expansion in Northeast China resulted in a 0. 85 Tg C loss of cropland NPP. (3) there was significant regional variation in cropland NPP loss. The low altitude and low slope regions experienced the most significant NPP loss. Meanwhile, large and developed cities caused more NPP losses, and cities with dispersed expansion had a broader impact on cropland productivity than cities with concentrated expansion. (4) when converting the loss of cropland NPP to crop yield, it revealed a reduction of 0.91 million tons in grain yield in Northeast China. These findings provide valuable insights for urban development and cropland protection strategies in Northeast China, offering crucial guidance for sustainable land use practices.

NDVI joint process-based models drive a learning ensemble model for accurately estimating cropland net primary productivity (NPP)

The accurate estimation of cropland net primary productivity (NPP) remains a significant challenge. We hypothesized that incorporating prior information on NPP simulated by process-based models into normalized difference vegetation index (NDVI) data would improve the accuracy of cropland ecosystem NPP estimations. We used NDVI, MNPP (NPP of process-based model), and SNPP (statistic-based NPP) data estimated by nine process-based models and yield statistics to build a learning ensemble of the random forest model (LERFM). We used the new model to re-evaluate the cropland NPP in China from 1982 to 2010. Large spatial discrepancies among MNPPs, which indicate uncertainties in cropland NPP estimation using different methods, were observed when compared to SNPP. The LERFM model showed a slightly underestimation of only −0.37%, while the multi-model average process-based model (MMEM) strongly underestimated −15.46% of the SNPP. LERFM accurately estimated cropland NPP with a high simulation skill score. A consistent increasing trend in the LERFM and MMEM NPP during 1982–2010 and a significant positive correlation (r = 0.795, p < 0.001) between their total NPP indicate that the LERFM model can better describe spatiotemporal dynamic changes in cropland NPP. This study suggests that a learning ensemble method that combines the NDVI and process-based simulation results can effectively improve cropland NPP.

Dams, cropland productivity, and economic development in China

We use satellite, hydrology, and census data from 1992 to 2014 to quantify dams' impacts on cropland productivity and economic development in China. We exploit a county's river gradient and elevation to address the endogeneity of dam placement. We find that an additional dam reduces a local county's cropland net primary production (NPP) and nighttime light (NTL)-based GDP by 13.7% and 2.9%, respectively. We also find that an additional dam increases a downstream county's NPP by 0.5% and has a positive yet insignificant impact on a downstream county's NTL-based GDP. Dynamic analysis shows that the positive impact of dams on downstream counties' cropland productivity and economic development takes around ten years to realize. We also find that dams have smaller positive impacts on agricultural productivity and economic growth than China's high-speed rail (HRS) construction.

Impact of urban expansion on grain production in the Japan Sea Rim region

The Japan Sea Rim (JSR) region is the core area for the future sustainable development of Northeast Asia. Effectively assessing the impact of urban expansion on grain production is of great significance for achieving sustainable development goals (SDGs), such as SDG2 Zero Hunger, and promoting sustainable development in Northeast Asia. Only a few studies have assessed the impact of urban expansion on grain production in the JSR. In this study, we initially analyzed urban expansion in the JSR for the period 1992 to 2050. Then, we assessed the impact of urban expansion on cropland net primary productivity (NPP) based on NPP data. Finally, we evaluated the impact of urban expansion on grain production in the JSR. The results indicated that urban land expanded from 21,509 km2 to 42,501 km2 in the JSR between 1992 and 2020 (an increase of 1.98 times), and the occupation of cropland caused by urban expansion resulted in a decrease of 2.21 million t in grain production. Urban land will continue to expand from 2020 to 2050, which will cause grain production to decrease by 1.68 (1.02–2.24) million t. China will experience the most serious loss of grain production, accounting for 62.93% (56.69–71.60%) of the total grain production loss in the JSR. Considering the situation for the period 1992–2020, the impact of urban expansion on grain production will be moderated in the future. Thus, the future grain production loss will be reduced by 24.18% (8.80–53.98%). However, future urban expansion will continue to have a negative impact on regional food security. Therefore, we suggest that urban development should be reasonably regulated and high-quality cropland should be protected. Meanwhile, we should strengthen international cooperation and optimize food and economic trade among different countries in the JSR to ensure food security and promote sustainable regional development.

Elevational Gradient of Climate-Driving Effects on Cropland Ecosystem Net Primary Productivity in Alpine Region of the Southwest China

Investigating elevational gradient of climate driving effects on cropland ecosystem net primary productivity (NPP) plays an important role in food security in alpine region. We simulated cropland NPP by coupling a remote sensing model with an ecosystem process model and explored elevational gradient of climate driving effects on it in an alpine region of the southwest China during 1981–2014. The results showed that cropland NPP increased significantly with a rate of 3.85 gC m−2 year−1 year−1 under significant increasing solar radiation and climate warming and drying, among which the increasing solar radiation was the main driving factor of the increasing NPP. The driving effect of climate warming on cropland NPP shifted from negative at low elevations to positive at high elevations, which was caused by the fragile ecosystem characteristics and frequent drought at low elevations and a higher temperature sensitivity of cropland ecosystem at high elevations. Different effects of climate warming on NPP change at different elevations caused different results when we analyzed the climate-driving effects on cropland NPP at different spatial scales. These results reminded us that we should take the elevational gradient of climate driving effects into account when we manage food security in the alpine region.

Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century

Spatiotemporally consistent data on global cropland extent is essential for tracking progress towards sustainable food production. In the present study, we present an analysis of global cropland area change for the first two decades of the twenty-first century derived from satellite data time-series. We estimate that, in 2019, the cropland area was 1,244 Mha with a corresponding total annual net primary production (NPP) of 5.5 Pg C year−1. From 2003 to 2019, cropland area increased by 9% and cropland NPP by 25%, primarily due to agricultural expansion in Africa and South America. Global cropland expansion accelerated over the past two decades, with a near doubling of the annual expansion rate, most notably in Africa. Half of the new cropland area (49%) replaced natural vegetation and tree cover, indicating a conflict with the sustainability goal of protecting terrestrial ecosystems. From 2003 to 2019, global per-capita cropland area decreased by 10% due to population growth. However, the per-capita annual cropland NPP increased by 3.5% as a result of intensified agricultural land use. The presented global, high-resolution, cropland map time-series supports monitoring of natural land appropriation at the local, national and international levels.

Quantification of the Relationship Among Cropland Area, Cropland Management Measures, and Cropland Productivity Using Panel Data Model

Cropland forms the material basis for human survival and is an important determinant of national food security. Cropland net primary productivity, which is the basis of food production and an important indicator of cropland productivity, reflects the production capacity of crops under natural conditions. However, currently, only limited knowledge was available on the relationship among cropland area, cropland management measures, and cropland productivity in China. Hence, in the current study, we used the panel data model to quantify the effects of cropland area and various cropland management measures (e.g., fertilizer use, irrigation area, agricultural machinery power, pesticide use, and agricultural film use) on cropland total net primary productivity (TNPP). Results revealed that for the entire cropland in China, an increase in cropland area, fertilizer use, agricultural machinery power, pesticide use, and agricultural film use increased cropland TNPP, whereas an increase in irrigation area decreased cropland TNPP. Further, in grain-producing areas, an increase in cropland area, fertilizer use, irrigation area, agricultural machinery power, pesticide use, and agricultural film use resulted in an increase in cropland TNPP. Moreover, in grain-balanced areas, an increase in cropland area, fertilizer use, pesticide use, and agricultural film use led to an increase in cropland TNPP, while an increase in irrigation area and agricultural machinery power reduced cropland TNPP. Finally, in grain-consuming areas, increases in cropland area, irrigation area, pesticide use, and agricultural film use caused an increase in cropland TNPP, whereas increases in fertilizer use and agricultural machinery power reduced cropland TNPP. Therefore, it is necessary to prevent the encroachment of fertile cropland and, simultaneously, implement scientific management measures based on local conditions in croplands.

The occupation of cropland by global urban expansion from 1992 to 2016 and its implications

Large-scale urban expansion worldwide has exerted great impacts on cropland and its net primary productivity (NPP), which can affect whether food security and sustainable development goals will be met at global and local scales. Although important, the impacts at the global scale over the last 25 years remain unclear. Based on the latest long-term dynamic urban expansion data, this study analyzed global urban expansion and its impacts on cropland NPP from 1992 to 2016 at multiple scales. The results showed that the expansion of urban land occupied a total of 159 170 km2 of cropland, accounting for 45.9% of the total expanded urban area. The cropland NPP decreased by 58.71 (56.52 ∼ 59.81, 95% confidence interval) TgC as a result of urban expansion, which represents approximately 0.42% (0.40% ∼ 0.43%) of the multiyear average of total cropland NPP from 2000 to 2015. If the cropland NPP losses were converted to the grain production (i.e. 1.44 × 107 tons), it is equivalent to the minimum annual food intake demands for at least 36 million people. More importantly, urban expansion is exacerbating the risk of food security in developing countries in Asia and Africa, such as China, Vietnam and Egypt. In the future, these countries should balance urban expansion with cropland protection by strictly restricting the occupation of cropland and encouraging smart urban growth.

Interaction effects of the main drivers of global climate change on spatiotemporal dynamics of high altitude ecosystem behaviors: process-based modeling.

Soil organic carbon and nitrogen (SOC-N) dynamics are indicative of the human-induced disturbances of the terrestrial ecosystems the quantification of which provides insights into interactions among drivers, pressures, states, impacts, and responses in a changing environment. In this study, a process-based model was developed to simulate the eight monthly outputs of net primary productivity (NPP), SOC-N pools, soil C:N ratio, soil respiration, total N emission, and sediment C-N transport effluxes for cropland, grassland, and forest on a hectare basis. The interaction effect of the climate change drivers of aridity, CO2 fertilization, land-use and land-cover change, and best management practices was simulated on high altitude ecosystems from 2018 to 2070. The best management practices were developed into a spatiotemporally composite index based on SOC-N stock saturation, 4/1000 initiative, and RUCLE-C factor. Our model predictions differed from the remotely sensed data in the range of - 64% (underestimation) for the cropland NPP to 142% (overestimation) for the grassland SOC pool as well as from the global mean values in the range of - 97% for the sediment C and N effluxes to 60% for the total N emission from the grassland. The interaction exerted the greatest negative impact on the monthly sediment N efflux, total N emission, and soil respiration from forest by - 90.5, - 82.7, and - 80.3% and the greatest positive impact on the monthly sediment C effluxes from cropland, grassland, and forest by 139.3, 137.1, and 133.3%, respectively, relative to the currently prevailing conditions.

Estimating global cropland production from 1961 to 2010

Abstract. Global cropland net primary production (NPP) has tripled over the last 50 years, contributing 17–45 % to the increase in global atmospheric CO2 seasonal amplitude. Although many regional-scale comparisons have been made between statistical data and modeling results, long-term national comparisons across global croplands are scarce due to the lack of detailed spatiotemporal management data. Here, we conducted a simulation study of global cropland NPP from 1961 to 2010 using a process-based model called Vegetation–Global Atmosphere–Soil (VEGAS) and compared the results with Food and Agriculture Organization of the United Nations (FAO) statistical data on both continental and country scales. According to the FAO data, the global cropland NPP was 1.3, 1.8, 2.2, 2.6, 3.0, and 3.6 PgC yr−1 in the 1960s, 1970s, 1980s, 1990s, 2000s, and 2010s, respectively. The VEGAS model captured these major trends on global and continental scales. The NPP increased most notably in the US Midwest, western Europe, and the North China Plain and increased modestly in Africa and Oceania. However, significant biases remained in some regions such as Africa and Oceania, especially in temporal evolution. This finding is not surprising as VEGAS is the first global carbon cycle model with full parameterization representing the Green Revolution. To improve model performance for different major regions, we modified the default values of management intensity associated with the agricultural Green Revolution differences across various regions to better match the FAO statistical data at the continental level and for selected countries. Across all the selected countries, the updated results reduced the RMSE from 19.0 to 10.5 TgC yr−1 (∼ 45 % decrease). The results suggest that these regional differences in model parameterization are due to differences in socioeconomic development. To better explain the past changes and predict the future trends, it is important to calibrate key parameters on regional scales and develop data sets for land management history.

Urban expansion brought stress to food security in China: Evidence from decreased cropland net primary productivity

Cropland net primary productivity (CNPP) is a crucial indicator of grain productivity and food security. However, assessments of the impact of urban expansion on the CNPP in China have been inadequate owing to data limitations. In this paper, our objective was to assess the impact of urban expansion on the CNPP in China from 1992 to 2015 in a spatially explicit manner. We first obtained the CNPP before urban expansion between 1992 and 2015 in China using the Carnegie-Ames-Stanford Approach (CASA) model. We then assessed the impact of urban expansion on the CNPP from 1992 to 2015 at multiple scales (the whole country, agricultural zones, and urban expansion hotspots) by combining the CNPP before urban expansion with the urban land coverage time series extracted from multi-source remotely sensed data. We found that the total loss of the CNPP due to urban expansion from 1992 to 2015 was 13.77TgC, which accounts for 1.88% of the CNPP before urban expansion in China. This CNPP loss resulted in a 12.45-million-ton decrease in grain production in China, corresponding to a reduction in the mean annual grain self-sufficiency rate of 2%. Therefore, we concluded that rapid urban expansion from 1992 to 2015 caused stress to China's food security. We suggest that it is still vital for China to effectively protect cropland to improve the urbanization level to 60% by 2020.

Atmospheric inversion of surface carbon flux with consideration of the spatial distribution of US crop production and consumption

Abstract. In order to improve quantification of the spatial distribution of carbon sinks and sources in the conterminous US, we conduct a nested global atmospheric inversion with detailed spatial information on crop production and consumption. County-level cropland net primary productivity, harvested biomass, soil carbon change, and human and livestock consumption data over the conterminous US are used for this purpose. Time-dependent Bayesian synthesis inversions are conducted based on CO2 observations at 210 stations to infer CO2 fluxes globally at monthly time steps with a nested focus on 30 regions in North America. Prior land surface carbon fluxes are first generated using a biospheric model, and the inversions are constrained using prior fluxes with and without adjustments for crop production and consumption over the 2002–2007 period. After these adjustments, the inverted regional carbon sink in the US Midwest increases from 0.25 ± 0.03 to 0.42 ± 0.13 Pg C yr−1, whereas the large sink in the US southeast forest region is weakened from 0.41 ± 0.12 to 0.29 ± 0.12 Pg C yr−1. These adjustments also reduce the inverted sink in the west region from 0.066 ± 0.04 to 0.040 ± 0.02 Pg C yr−1 because of high crop consumption and respiration by humans and livestock. The general pattern of sink increases in crop production areas and sink decreases (or source increases) in crop consumption areas highlights the importance of considering the lateral carbon transfer in crop products in atmospheric inverse modeling, which provides a reliable atmospheric perspective of the overall carbon balance at the continental scale but is unreliable for separating fluxes from different ecosystems.

Cropland Dynamics and their Influence on the Productivity in Northern Shaanxi, China, for the Past 20 Years: Based on Remotely Sensed Data

In this paper, we mainly focused our research on northern Shaanxi district, which is a pilot area of the Grain for Green Project. We compared the spatial distribution patterns of croplands and their productivity for the past 20 years (from the end of the 1980s to 2010). Cropland dynamics for the past 20 years were Interpreted from medium- and high-resolutlon remote sensing images (Landsat TM/ETM ). In addition, using the GLO-PEM and AGRO-VPM models with a medium resolution and long time series remote sensing dataset (AVHRR/MODIS), net primary productivity (NPP) and its relationship with cropland were estimated. Finally, the effect of cropland change on productivity was analyzed. The results show that during the first decade of the research period, cropland area and productivity in northern Shaanxi experienced a small boost, while in the latter decade, both cropland area and NPP were significantly reduced. The main cause of the increase in cropland was the reclamation of large area of grassland and unutilized land to meet the food demands of the local population as well as to compensate for the occupation of urban constructions. While the main cause of the decrease in cropland was the implementation of the Grain for Green Project. In addition, urbanization was also a key factor. Overall, during the past 20 years, the total area of cropland in northern Shaanxi decreased by 42.56%, and cropland NPP dropped by 41.90%. This study is of great importance for the assessment of regional cropland security, food security and scientific planning of regional land use.

Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States

Accurately quantifying the spatial and temporal variability of net primary production (NPP) for croplands is essential to understand regional cropland carbon dynamics. We compared three NPP estimates for croplands in the Midwestern United States: inventory-based estimates using crop yield data from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS); estimates from the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product; and estimates from the General Ensemble biogeochemical Modeling System (GEMS) process-based model. The three methods estimated mean NPP in the range of 469–687gCm−2yr−1 and total NPP in the range of 318–490TgCyr−1 for croplands in the Midwest in 2007 and 2008. The NPP estimates from crop yield data and the GEMS model showed the mean NPP for croplands was over 650gCm−2yr−1 while the MODIS NPP product estimated the mean NPP was less than 500gCm−2yr−1. MODIS NPP also showed very different spatial variability of the cropland NPP from the other two methods. We found these differences were mainly caused by the difference in the land cover data and the crop specific information used in the methods. Our study demonstrated that the detailed mapping of the temporal and spatial change of crop species is critical for estimating the spatial and temporal variability of cropland NPP. We suggest that high resolution land cover data with species–specific crop information should be used in satellite-based and process-based models to improve carbon estimates for croplands.

Estimating crop net primary production using national inventory data and MODIS-derived parameters

National estimates of spatially-resolved cropland net primary production (NPP) are needed for diagnostic and prognostic modeling of carbon sources, sinks, and net carbon flux between land and atmosphere. Cropland NPP estimates that correspond with existing cropland cover maps are needed to drive biogeochemical models at the local scale as well as national and continental scales. Existing satellite-based NPP products tend to underestimate NPP on croplands. An Agricultural Inventory-based Light Use Efficiency (AgI-LUE) framework was developed to estimate individual crop biophysical parameters for use in estimating crop-specific NPP over large multi-state regions. The method is documented here and evaluated for corn (Zea mays L.) and soybean (Glycine max L. Merr.) in Iowa and Illinois in 2006 and 2007. The method includes a crop-specific Enhanced Vegetation Index (EVI), shortwave radiation data estimated using the Mountain Climate Simulator (MTCLIM) algorithm, and crop-specific LUE per county. The combined aforementioned variables were used to generate spatially-resolved, crop-specific NPP that corresponds to the Cropland Data Layer (CDL) land cover product. Results from the modeling framework captured the spatial NPP gradient across croplands of Iowa and Illinois, and also represented the difference in NPP between years 2006 and 2007. Average corn and soybean NPP from AgI-LUE was 917gCm−2yr−1 and 409gCm−2yr−1, respectively. This was 2.4 and 1.1 times higher, respectively, for corn and soybean compared to the MOD17A3 NPP product. Site comparisons with flux tower data show AgI-LUE NPP in close agreement with tower-derived NPP, lower than inventory-based NPP, and higher than MOD17A3 NPP. The combination of new inputs and improved datasets enabled the development of spatially explicit and reliable NPP estimates for individual crops over large regional extents.

SATELLITE DRIVEN ESTIMATION OF PRIMARY PRODUCTIVITY OF AGROECOSYSTEMS IN INDIA

Abstract. Earth observation driven ecosystem modeling have played a major role in estimation of carbon budget components such as gross primary productivity (GPP) and net primary production (NPP) over terrestrial ecosystems, including agriculture. The present study therefore evaluate satellite-driven vegetation photosynthesis (VPM) model for GPP estimation over agro-ecosystems in India by using time series of the Normalized Difference Vegetation Index (NDVI) from SPOT-VEGETATION, cloud cover observation from MODIS, coarse-grid C3/C4 crop fraction and decadal grided databases of maximum and minimum temperatures. Parameterization of VPM parameters e.g. maximum light use efficiency (ε*) and Tscalar was done based on eddy-covariance measurements and literature survey. Incorporation of C3/C4 crop fraction is a modification to commonly used constant maximum LUE. Modeling results from VPM captured very well the geographical pattern of GPP and NPP over cropland in India. Well managed agro-ecosystems in Trans-Gangetic and upper Indo-Gangetic plains had the highest magnitude of GPP with peak GPP during kharif occurs in sugarcane-wheat system (western UP) and it occurs in rice-wheat system (Punjab) during Rabi season. Overall, croplands in these plains had more annual GPP (> 1000 g C m-2) and NPP (> 600 g C m-2) due to input-intensive cultivation. Desertic tracts of western Rajasthan showed the least GPP and NPP values. Country-level contribution of croplands to national GPP and NPP amounts to1.34 Pg C year-1 and 0.859 Pg C year-1, respectively. Modeled estimates of cropland NPP agrees well with ground-based estimates for north-western India (R2 = 0.63 and RMSE = 108 g C m-2). Future research will focus on evaluating the VPM model with medium resolution sensors such as AWiFS and MODIS for rice-wheat system and validating with eddy-covariance measurements.

Estimating net primary production and annual plant carbon inputs, and modelling future changes in soil carbon stocks in arable farmlands of northern Japan

Soil C sequestration in croplands is deemed to be one of the most promising greenhouse gas mitigation options for Japan's agriculture. In this context, changes in soil C stocks in northern Japan's arable farming area over the period of 1971–2010, specifically in the region's typical Andosol (volcanic ash-derived) and non-Andosol soils, were simulated using soil-type-specific versions of the Rothamsted carbon model (RothC). The models were then used to predict the effects, over the period of 2011–2050, of three potential management scenarios: (i) baseline: maintenance of present crop residue returns and green manure crops, as well as composted cattle manure C inputs (24–34 Mg ha −1 yr −1 applied on 3–55% of arable land according to crop), (ii) cattle manure: all arable fields receive 20 Mg ha −1 yr −1 of composted cattle manure, increased C inputs from crop residues and present C inputs from green manure are assumed, and (iii) minimum input: all above-ground crop residues removed, no green manure crop, no cattle manure applied. Above- and below-ground residue biomass C inputs contributed by 8 major crops, and oats employed as a green manure crop, were drawn from yield statistics recorded at the township level and crop-specific allometric relationships (e.g. ratio of above-ground residue biomass to harvested biomass on a dry weight basis). Estimated crop net primary production (NPP) ranged from 1.60 Mg C ha −1 yr −1 for adzuki bean to 8.75 Mg C ha −1 yr −1 for silage corn. For the whole region (143 × 10 3 ha), overall NPP was estimated at 952 ± 60 Gg C yr −1 (6.66 ± 0.42 Mg C ha −1 yr −1). Plant C inputs to the soil also varied widely amongst the crops, ranging from 0.50 Mg C ha −1 yr −1 for potato to 3.26 Mg C ha −1 yr −1 for winter wheat. Annual plant C inputs to the soil were estimated at 360 ± 45 Gg C yr −1 (2.52 ± 0.32 Mg C ha −1 yr −1), representing 38% of the cropland NPP. The RothC simulations suggest that the region's soil C stock (0–30 cm horizon), across all soils, has decreased from 13.96 Tg C (107.5 Mg C ha −1 yr −1) in 1970 to 12.46 Tg C (96.0 Mg C ha −1 yr −1) in 2010. For the baseline, cattle manure and minimum input scenarios, soil C stocks of 12.13, 13.27 and 9.82 Tg C, respectively, were projected for 2050. Over the period of 2011–2050, compared to the baseline scenario, soil C was sequestered (+0.219 Mg C ha −1 yr −1) by enhanced cattle manure application, but was lost (−0.445 Mg C ha −1 yr −1) under the minimum input scenario. The effect of variations of input data (monthly mean temperature, monthly precipitation, plant C inputs and cattle manure C inputs) on the uncertainty of model outputs for each scenario was assessed using a Monte Carlo approach. Taking into account the uncertainty (standard deviation as % of the mean) for the model's outputs for 2050 (5.1–6.1%), it is clear that the minimum input scenario would lead to a rapid decrease in soil C stocks for arable farmlands in northern Japan.

Cropland carbon fluxes in the United States: increasing geospatial resolution of inventory‐based carbon accounting

Net annual soil carbon change, fossil fuel emissions from cropland production, and cropland net primary production were estimated and spatially distributed using land cover defined by NASA's moderate resolution imaging spectroradiometer (MODIS) and by the USDA National Agricultural Statistics Service (NASS) cropland data layer (CDL). Spatially resolved estimates of net ecosystem exchange (NEE) and net ecosystem carbon balance (NECB) were developed. The purpose of generating spatial estimates of carbon fluxes, and the primary objective of this research, was to develop a method of carbon accounting that is consistent from field to national scales. NEE represents net on-site vertical fluxes of carbon. NECB represents all on-site and off-site carbon fluxes associated with crop production. Estimates of cropland NEE using moderate resolution (approximately 1 km2) land cover data were generated for the conterminous United States and compared with higher resolution (30-m) estimates of NEE and with direct measurements of CO2 flux from croplands in Illinois and Nebraska, USA. Estimates of NEE using the CDL (30-m resolution) had a higher correlation with eddy covariance flux tower estimates compared with estimates of NEE using MODIS. Estimates of NECB are primarily driven by net soil carbon change, fossil fuel emissions associated with crop production, and CO2 emissions from the application of agricultural lime. NEE and NECB for U.S. croplands were -274 and 7 Tg C/yr for 2004, respectively. Use of moderate- to high-resolution satellite-based land cover data enables improved estimates of cropland carbon dynamics.

Estimation and analysis of terrestrial net primary productivity over India by remote-sensing-driven terrestrial biosphere model

In the present study, the Carnegie-Ames-Stanford Approach (CASA), a terrestrial biosphere model, has been used to investigate spatiotemporal pattern of net primary productivity (NPP) during 2003 over the Indian subcontinent. The model drivers at 2-min spatial resolution were derived from National Oceanic and Atmospheric Administration advanced very high resolution radiometer normalized difference vegetation index, weather inputs, and soil and land cover maps. The annual NPP was estimated to be 1.57 Pg C (at the rate of 544 g C m(-2)), of which 56% contributed by croplands (with 53% of geographic area of the country (GAC)), 18.5% by broadleaf deciduous forest (15% of GAC), 10% by broadleaf evergreen forest (5% of GAC), and 8% by mixed shrub and grassland (19% of GAC). There is very good agreement between the modeled NPP and ground-based cropland NPP estimates over the western India (R2=0.54; p=0.05). The comparison of CASA-based annual NPP estimates with the similar products from other operational algorithms such as C-fix and Moderate Resolution Imaging Spectroradiometer (MODIS) indicate that high agreement exists between the CASA and MODIS products over all land covers of the country, while agreement between CASA and C-Fix products is relatively low over the region dominated by agriculture and grassland, and the agreement is very low over the forest land. Sensitivity analysis suggest that the difference could be due to inclusion of variable light use efficiency (LUE) across different land cover types and environment stress scalars as downregulator of NPP in the present CASA model study. Sensitivity analysis further shows that the CASA model can overestimate the NPP by 50% of the national budget in absence of downregulators and underestimate the NPP by 27% of the national budget by the use of constant LUE (0.39 gC MJ(-1)) across different vegetation cover types.

Spatiotemporal patterns of cropland area and net primary production in the central United States estimated from USDA agricultural information

The central United States, which is dominated by agriculture, has been selected as the first North American Carbon Program intensive campaign. Data sets that describe spatiotemporal variability in carbon fluxes are needed to support this campaign. Here we report the behavior of county cropland net primary production (NPP) in the first intensive region derived using USDA information together with crop‐specific parameters that convert agronomic data into carbon fluxes. Total cropland area in the eight‐state region was ∼550,000 km2 (40% of total area), with some interannual variability but no temporal trend from 1972 to 2001. Regional production (P) was 0.3 Pg C yr−1 in the late 1990s, roughly 64% of the total US crop production. P was highest in the central counties (>1.2 Tg C yr−1). In contrast to area, both NPP (flux per unit area) and P (spatially aggregated flux) increased during the study period (46 and 51%, respectively). Corn was the dominant crop type grown in the region, contributing 58% of the total production, with soybeans second most productive but substantially less (20%) despite similar harvested area. Maximum year‐to‐year variability in P was high, generally greater than 30% for most counties, though exceeding 80% for some counties.