Simulation of spatial variability in crop leaf area index and yield using agroecosystem modeling and geophysics-based quantitative soil information

Agroecosystem models that simulate crop growth as a function of weather conditionsand soil characteristics are among the most promising tools for improving crop yield and achieving more sustainable agricultural production systems. This study aims at using spatially distributed crop growth simulations to investigate how field-scale patterns in soil properties obtained using geophysical mapping affect the spatial variability of soil water content dynamics and growth of crops at the square kilometer scale. For this, a geophysics-based soil map was intersected with land use information. Soilhydraulic parameters were calculated using pedotransfer functions. Simulations of soilwater content dynamics performed with the agroecosystem model AgroC were com-pared with soil water content measured at two locations, resulting in RMSE of 0.032and of 0.056 cm3cm−3, respectively. The AgroC model was then used to simulate thegrowth of sugar beet (Beta vulgaris L.), silage maize (Zea maysL.), potato (SolanumtuberosumL.), winter wheat (Triticum aestivumL.), winter barley (Hordeum vulgareL.), and winter rapeseed (Brassica napusL.) in the 1- by 1-km study area. It was found that the simulated leaf area index (LAI) was affected by the magnitude of simulated water stress, which was a function of both the crop type and soil characteristics. Simulated LAI was generally consistent with the observed LAI calculated from normalized difference vegetation index (LAINDVI) obtained from RapidEye satellite data. Finally, maps of simulated agricultural yield were produced for four crops, and it was found that simulated yield matched well with actual harvest data and literature values. Therefore, it was concluded that the information obtained from geophysics-based soilmapping was valuable for practical agricultural applications

The effects of CO2, climate and land-use on terrestrial carbon balance, 1920-1992: An analysis with four process-based ecosystem models

The concurrent effects of increasing atmospheric CO2 concentration, climate variability, and cropland establishment and abandonment on terrestrial carbon storage between 1920 and 1992 were assessed using a standard simulation protocol with four process-based terrestrial biosphere models. Over the long-term(1920–1992), the simulations yielded a time history of terrestrial uptake that is consistent (within the uncertainty) with a long-term analysis based on ice core and atmospheric CO2 data. Up to 1958, three of four analyses indicated a net release of carbon from terrestrial ecosystems to the atmosphere caused by cropland establishment. After 1958, all analyses indicate a net uptake of carbon by terrestrial ecosystems, primarily because of the physiological effects of rapidly rising atmospheric CO2. During the 1980s the simulations indicate that terrestrial ecosystems stored between 0.3 and 1.5 Pg C yr−1, which is within the uncertainty of analysis based on CO2 and O2 budgets. Three of the four models indicated (in accordance with O2 evidence) that the tropics were approximately neutral while a net sink existed in ecosystems north of the tropics. Although all of the models agree that the long-term effect of climate on carbon storage has been small relative to the effects of increasing atmospheric CO2 and land use, the models disagree as to whether climate variability and change in the twentieth century has promoted carbon storage or release. Simulated interannual variability from 1958 generally reproduced the El Niño/Southern Oscillation (ENSO)-scale variability in the atmospheric CO2 increase, but there were substantial differences in the magnitude of interannual variability simulated by the models. The analysis of the ability of the models to simulate the changing amplitude of the seasonal cycle of atmospheric CO2 suggested that the observed trend may be a consequence of CO2 effects, climate variability, land use changes, or a combination of these effects. The next steps for improving the process-based simulation of historical terrestrial carbon include (1) the transfer of insight gained from stand-level process studies to improve the sensitivity of simulated carbon storage responses to changes in CO2 and climate, (2) improvements in the data sets used to drive the models so that they incorporate the timing, extent, and types of major disturbances, (3) the enhancement of the models so that they consider major crop types and management schemes, (4) development of data sets that identify the spatial extent of major crop types and management schemes through time, and (5) the consideration of the effects of anthropogenic nitrogen deposition. The evaluation of the performance of the models in the context of a more complete consideration of the factors influencing historical terrestrial carbon dynamics is important for reducing uncertainties in representing the role of terrestrial ecosystems in future projections of the Earth system

The carbon budget of a winter wheat field: An eddy covariance analysis of seasonal and inter-annual variability

Arable land occupies large areas of global land surface and hence plays an important role in the terrestrial carbon cycle. Therefore agro-ecosystems show a high potential of mitigating greenhouse gas emissions while optimizing agricultural management. Hence, there is a growing interest in analyzing and understanding carbon fluxes from arable land as affected by regional environmental as well as management conditions. The major goal of this study is to use a two year data set of eddy covariance measurements (October 2007 to October 2009) on a winter wheat field located in Western Germany to assess the seasonal and inter-annual variability of carbon fluxes as affected by meteorological variables and land management. During the study period, which was comprised of two full growing seasons, eddy covariance measurements together with measurements of various soil, plant, and meteorological data were performed. Flux partitioning and gap filling methods including uncertainty estimates were applied to derive complete time series of net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration (R-eco). Despite different management dates and slightly different meteorological conditions, annual NEE resulted in 270 g C m(-2) in both years. Although the period from sowing to harvesting was more than 20 days shorter in the first year, due to the later start of senescence, GPP was higher by 220 g C m(-2). In the annual carbon budget this was compensated by a stronger heterotrophic respiration after the harvest of sugar beet grown on the field before the study period. Taking into account the carbon losses due to removal of biomass during harvest, the winter wheat field acts as a carbon source with respective net biome productivities (NBP) of 246 and 201 g C m(-2) a(-1). To complete the carbon balance, releases due to energy consumption associated with crop production are taken into account. However, the relatively large carbon loss was probably, to a large extent, compensated by carbon input from plant residues left on the field after preceding sugar beet harvest. This underlines the importance of multi-annual measurements taking full crop rotations into account. (C) 2012 Elsevier B.V. All rights reserved

Spatio-temporal soil moisture patterns - A meta-analysis using plot to catchment scale data

Soil moisture is a key variable in hydrology, meteorology and agriculture. It is influenced by many factors, such as topography, soil properties, vegetation type, management, and meteorological conditions. The role of these factors in controlling the spatial patterns and temporal dynamics is often not well known. The aim of the current study is to analyze spatio-temporal soil moisture patterns acquired across a variety of land use types, on different spatial scales (plot to meso-scale catchment) and with different methods (point measurements, remote sensing, and modeling). We apply a uniform set of tools to determine method specific effects, as well as site and scale specific controlling factors. Spatial patterns of soil moisture and their temporal development were analyzed using nine different datasets from the Rur catchment in Western Germany. For all datasets we found negative linear relationships between the coefficient of variation and the mean soil moisture, indicating lower spatial variability at higher mean soil moisture. For a forest sub-catchment compared to cropped areas, the offset of this relationship was larger, with generally larger variability at similar mean soil moisture values. Using a geostatistical analysis of the soil moisture patterns we identified three groups of datasets with similar values for sill and range of the theoretical variogram: (i) modeled and measured datasets from the forest sub-catchment (patterns mainly influenced by soil properties and topography), (ii) remotely sensed datasets from the cropped part of the Rur catchment (patterns mainly influenced by the land-use structure of the cropped area), and (iii) modeled datasets from the cropped part of the Rur catchment (patterns mainly influenced by large scale variability of soil properties). A fractal analysis revealed that all analyzed soil moisture patterns showed a multifractal behavior, with at least one scale break and generally high fractal dimensions. Corresponding scale breaks were found between different datasets. The factors causing these scale breaks are consistent with the findings of the geostatistical analysis. Furthermore, the joined analysis of the different datasets showed that small differences in soil moisture dynamics, especially at the upper and lower bounds of soil moisture (at maximum porosity and wilting point of the soils) can have a large influence on the soil moisture patterns and their autocorrelation structure. Depending on the prevalent type of land use and the time of year, vegetation causes a decrease or an increase of spatial variability in the soil moisture pattern