Gross primary production and ecosystem respiration of irrigated and rainfed maize–soybean cropping systems over 8 years

The objective of this study is to examine interannual variability of carbon dioxide exchange and relevant controlling factors in irrigated and rainfed maize–soybean agroecosystems. The mean annual gross primary production (GPP) of irrigated and rainfed maize was 1796 ± 92 g C m−2 y−1 (±standard deviation) and 1536 ± 74 g C m−2 y−1, respectively. Mean annual GPP of soybean (average of irrigated and rainfed crops) was about 56% that of maize. Light use efficiency of maize and soybean during clear sky conditions were 1.96 ± 0.10 and 1.37 ± 0.06 g C MJ−1, respectively. A light use efficiency model, incorporating sensitivity to diffuse light, provided a reasonable simulation of daily GPP of maize and soybean (r2 = 0.89–0.98 and 0.85–0.97, respectively). Simulated growing season GPP totals were within about 10% of the measured values. The green leaf area index (LAI) played a dominant role in explaining interannual variability of GPP in maize. For soybean, both LAI and PAR contributed to the interannual variability. Mean growing season ecosystem respiration (Re) totals were 1029 ± 46 g C m−2 for irrigated maize and 872 ± 29 g C m−2 for rainfed maize. The growing season Re total of soybean (average of irrigated and rainfed crops) was about 78% that of maize. A relationship, based on a reference soil respiration (Re20), air temperature (Ta), and LAI, simulated daily growing season Re reasonably well for maize and soybean (r2 = 0.77–0.91 and 0.51–0.94, respectively). Modeled Re totals during the growing season were generally within 10% of the measured values. Variations in the LAI and Re20 explained the majority of the interannual variability in growing season Re for maize. In addition to LAI and Re20, Ta also contributed to the soybean Re variability. Non growing season Re contributed 10–20% and 17–24% of annual Re in maize and soybean, respectively and was primarily controlled by air temperature and residue biomass (r2 ∼ 81%). About 70% of maize GPP was lost in Re, resulting in the mean annual net ecosystem CO2 production (NEP) of 552 ± 73 g C m−2 y−1 for irrigated maize and 471 ± 52 g C m−2 y−1 for rainfed maize. For soybean, however, most of the annual GPP was lost in Re resulting in a mean annual NEP of −57 ± 43 and 10 ± 52 g C m−2 y−1 for irrigated and rainfed soybean, respectively. In general, as compared to Re, GPP contributed more to explaining the departures (ΔNEP) of NEP from the 4-year mean for maize. Both GPP and Re contributed to the ΔNEP for soybean. Results on the net biome production (NBP) indicated that the irrigated maize–soybean rotation was initially a moderate source of carbon; however, the system appears to be approaching near C neutral recently. The rainfed maize–soybean rotation is approximately C neutral

Coupling of carbon dioxide and water vapor exchanges of irrigated and rainfed maize–soybean cropping systems and water productivity

Continuous measurements of CO2 and water vapor exchanges made in three cropping systems (irrigated continuous maize, irrigated maize–soybean rotation, and rainfed maize–soybean rotation) in eastern Nebraska, USA during 6 years are discussed. Close coupling between seasonal distributions of gross primary production (GPP) and evapotranspiration (ET) were observed in each growing season. Mean growing season totals of GPP in irrigated maize and soybean were 1738 ± 114 and 996 ± 69 g C m−2, respectively (±standard deviation). Corresponding mean values of growing season ET totals were 545 ± 27 and 454 ± 23 mm, respectively. Irrigation affected GPP and ET similarly, both growing season totals were about 10% higher than those of corresponding rainfed crops. Maize, under both irrigated and rainfed conditions, fixed 74% more carbon than soybean while using only 12–20% more water. The green leaf area index (LAI) explained substantial portions (91% for maize and 90% for soybean) of the variability in GPPPAR (GPP over a narrow range of incident photosynthetically active radiation) and in ET/ETo (71% for maize and 75% for soybean, ETo is the reference evapotranspiration). Water productivity (WP or water use efficiency) is defined here as the ratio of cumulative GPP or above-ground biomass and ET (photosynthetic water productivity = ∑GPP/∑ET and biomass water productivity = above-ground biomass/∑ET). When normalized by ETo, the photosynthetic water productivity (WPETo) was 18.4 ± 1.5 g C m−2 for maize and 12.0 ± 1.0 g C m−2 for soybean. When normalized by ETo, the biomass water productivity (WPETo) was 27.5 ± 2.3 g DM m−2 for maize and 14.1 ± 3.1 g DM m−2 for soybean. Comparisons of these results, among different years of measurement and management practices (continuous vs rotation cropping, irrigated vs rainfed) in this study and those from other locations, indicated the conservative nature of normalized water productivity, as also pointed out by previous investigators

Coupling of carbon dioxide and water vapor exchanges of irrigated and rainfed maize–soybean cropping systems and water productivity

Continuous measurements of CO2 and water vapor exchanges made in three cropping systems (irrigated continuous maize, irrigated maize–soybean rotation, and rainfed maize–soybean rotation) in eastern Nebraska, USA during 6 years are discussed. Close coupling between seasonal distributions of gross primary production (GPP) and evapotranspiration (ET) were observed in each growing season. Mean growing season totals of GPP in irrigated maize and soybean were 1738 ± 114 and 996 ± 69 g C m−2, respectively (±standard deviation). Corresponding mean values of growing season ET totals were 545 ± 27 and 454 ± 23 mm, respectively. Irrigation affected GPP and ET similarly, both growing season totals were about 10% higher than those of corresponding rainfed crops. Maize, under both irrigated and rainfed conditions, fixed 74% more carbon than soybean while using only 12–20% more water. The green leaf area index (LAI) explained substantial portions (91% for maize and 90% for soybean) of the variability in GPPPAR (GPP over a narrow range of incident photosynthetically active radiation) and in ET/ETo (71% for maize and 75% for soybean, ETo is the reference evapotranspiration). Water productivity (WP or water use efficiency) is defined here as the ratio of cumulative GPP or above-ground biomass and ET (photosynthetic water productivity = ∑GPP/∑ET and biomass water productivity = above-ground biomass/∑ET). When normalized by ETo, the photosynthetic water productivity (WPETo) was 18.4 ± 1.5 g C m−2 for maize and 12.0 ± 1.0 g C m−2 for soybean. When normalized by ETo, the biomass water productivity (WPETo) was 27.5 ± 2.3 g DM m−2 for maize and 14.1 ± 3.1 g DM m−2 for soybean. Comparisons of these results, among different years of measurement and management practices (continuous vs rotation cropping, irrigated vs rainfed) in this study and those from other locations, indicated the conservative nature of normalized water productivity, as also pointed out by previous investigators

Fitting measured evapotranspiration data to the FAO56 dual crop coefficient method

The FAO-56 publication of the UN Food and Agriculture Organization contains guidelines on constructing and applying a ‘dual crop coefficient’ method to characterize the behavior of evapotranspiration (ET) on a day to day basis. The dual crop coefficient (Kc) method substantially improves the ability to fit simulated with measured data, as compared to the ‘single’ Kc method, by partitioning evaporation from soil (Es) from transpiration from vegetation. This permits the separate estimation of Es when there are known wetting events from precipitation and irrigation and assists in explaining behavior of measured data. The application of the dual Kc method is relatively straight forward, especially when applied using the straight-line segment method for the basal Kc curve, Kcb. Illustrations are given on fitting the dual Kc method and Kcb curve to daily ET data for irrigated and rainfed corn crops near Mead, Nebraska measured by eddy covariance and sensitivity to various soil and root zone parameters. Assessment of transferring Kcb curve parameters to other fields and years indicates that soil and root zone parameters are relatively transferrable with little modification, whereas lengths of the four crop growth stages do vary from year to year due to differences in cultivar type and possibly differences in weather

Prediction of Biome-Specific Potential Evapotranspiration in Mongolia under a Scarcity of Weather Data

We propose practical guidelines to predict biome-specific potential evapotranspiration (ETp) from the knowledge of grass-reference evapotranspiration (ET0) and a crop coefficient (Kc) in Mongolia. A paucity of land-based weather data hampers use of the Penman–Monteith equation (FAO-56 PM) based on the Food and Agriculture Organization (FAO) guidelines to predict daily ET0. We found that the application of the Hargreaves equation provides ET0 estimates very similar to those from the FAO-56 PM approach. The Kc value is tabulated only for crops in the FAO-56 guidelines but is unavailable for steppe grasslands. Therefore, we proposed a new crop coefficient, Kc adj defined by (a) net solar radiation in the Gobi Desert (Kc adjD) or (b) leaf area index in the steppe region (Kc adjS) in Mongolia. The mean annual ETp obtained using our approach was compared to that obtained by FAO-56 guidelines for forages (not steppe) based on tabulated Kc values in 41 locations in Mongolia. We found the differences are acceptable (RMSE of 0.40 mm d-1) in northern Mongolia under high vegetation cover but rather high (RMSE of 1.69 and 2.65 mm d-1) in central and southern Mongolia. The FAO aridity index (AI) is empirically related to the ETp/ET0 ratio. Approximately 80% and 54% reduction of ET0 was reported in the Gobi Desert and in the steppe locations, respectively. Our proposed Kc adj can be further improved by considering local weather data and plant phenological characteristics

Evaluation of a Hybrid Reflectance-Based Crop Coefficient and Energy Balance Evapotranspiration Model for Irrigation Management

Accurate generation of spatial soil water maps is useful for many types of irrigation management. A hybrid remote sensing evapotranspiration (ET) model combining reflectance-based basal crop coefficients (Kcbrf) and a two-source energy balance (TSEB) model was modified and validated for use in real-time irrigation management. We modeled spatial ET for maize and soybean fields in eastern Nebraska for the 2011-2013 growing seasons. We used Landsat 5, 7, and 8 imagery as remote sensing inputs. In the TSEB, we used the Priestly-Taylor (PT) approximation for canopy latent heat flux, as in the original model formulations. We also used the Penman-Monteith (PM) approximation for comparison. We compared energy balance fluxes and computed ET with measurements from three eddy covariance systems within the study area. Net radiation was underestimated by the model when data from a local weather station were used as input, with mean bias error (MBE) of -33.8 to -40.9 W m-2. The measured incident solar radiation appeared to be biased low. The net radiation model performed more satisfactorily when data from the eddy covariance flux towers were input into the model, with MBE of 5.3 to 11.2 W m-2. We removed bias in the daily energy balance ET using a dimensionless multiplier that ranged from 0.89 to 0.99. The bias-corrected TSEB ET, using weather data from a local weather station and with local ground data in thermal infrared imagery corrections, had MBE = 0.09 mm d-1 (RMSE = 1.49 mm d-1) for PM and MBE = 0.04 mm d-1 (RMSE = 1.18 mm d-1) for PT. The hybrid model used statistical interpolation to combine the two ET estimates. We computed weighting factors for statistical interpolation to be 0.37 to 0.50 for the PM method and 0.56 to 0.64 for the PT method. Provisions were added to the model, including a real-time crop coefficient methodology, which allowed seasonal crop coefficients to be computed with relatively few remote sensing images. This methodology performed well when compared to basal crop coefficients computed using a full season of input imagery. Water balance ET compared favorably with the eddy covariance data after incorporating the TSEB ET. For a validation dataset, the magnitude of MBE decreased from -0.86 mm d-1 (RMSE = 1.37 mm d-1) for the Kcbrfalone to -0.45 mm d-1 (RMSE = 0.98 mm d-1) and -0.39 mm d-1 (RMSE = 0.95 mm d-1) with incorporation of the TSEB ET using the PM and PT methods, respectively. However, the magnitudes of MBE and RMSE were increased for a running average of daily computations in the full May-October periods. The hybrid model did not necessarily result in improved model performance. However, the water balance model is adaptable for real-time irrigation scheduling and may be combined with forecasted reference ET, although the low temporal frequency of satellite imagery is expected to be a challenge in real-time irrigation management

Carbon Flux Phenology from the Sky: Evaluation for Maize and Soybean

Carbon flux phenology is widely used to understand carbon flux dynamics and surface exchange processes. Vegetation phenology has been widely evaluated by remote sensors; however, very few studies have evaluated the use of vegetation phenology for identifying carbon flux phenology. Currently available techniques to derive net ecosystem exchange (NEE) from a satellite image use a single generic modeling subgroup for agricultural crops. But, carbon flux phenological processes vary highly with crop types and land management practices; this paper reexamines this assumption. Presented here are an evaluation of ground-truth remotely sensed vegetation indices with in situ NEE measurements and an identification of vegetation indices for estimating carbon flux phenology metrics by crop type. Results show that the performance of different vegetation indices as an indicator of phenology varies with crop type, particularly when identifying the start of a season and the peak of a season. Maize fields require vegetation indices that make use of the near-infrared and red reflectance bands, while soybean fields require those making use of the shortwave infrared (IR) and near-IR bands. In summary, the study identifies how to best utilize remote sensing technology as a crop-specific measurement tool

Estimating seasonal evapotranspiration from temporal satellite images

Estimating seasonal evapotranspiration (ET) has many applications in water resources planning and management, including hydrological and ecological modeling. Availability of satellite remote sensing images is limited due to repeat cycle of satellite or cloud cover. This study was conducted to determine the suitability of different methods namely cubic spline, fixed, and linear for estimating seasonal ET from temporal remotely sensed images. Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model in conjunction with the wet METRIC (wMETRIC), a modified version of the METRIC model, was used to estimate ET on the days of satellite overpass using eight Landsat images during the 2001 crop growing season in Midwest USA. The model-estimated daily ET was in good agreement (R2 = 0.91) with the eddy covariance tower-measured daily ET. The standard error of daily ET was 0.6 mm (20%) at three validation sites in Nebraska, USA. There was no statistically significant difference (P\u3e0.05) among the cubic spline, fixed, and linear methods for computing seasonal (July–December) ET from temporal ET estimates. Overall, the cubic spline Estimating seasonal evapotranspiration (ET) has many applications in water resources planning and management, including hydrological and ecological modeling. Availability of satellite remote sensing images is limited due to repeat cycle of satellite or cloud cover. This study was conducted to determine the suitability of different methods namely cubic spline, fixed, and linear for estimating seasonal ET from temporal remotely sensed images. Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model in conjunction with the wet METRIC (wMETRIC), a modified version of the METRIC model, was used to estimate ET on the days of satellite overpass using eight Landsat images during the 2001 crop growing season in Midwest USA. The model-estimated daily ET was in good agreement (R2 = 0.91) with the eddy covariance tower-measured daily ET. The standard error of daily ET was 0.6 mm (20%) at three validation sites in Nebraska, USA. There was no statistically significant difference (P[0.05) among the cubic spline, fixed, and linear methods for computing seasonal (July–December) ET from temporal ET estimates. Overall, the cubic splin

Modeling gross primary production of irrigated and rain-fed maize using MODIS imagery and CO\u3csub\u3e2\u3c/sub\u3e flux tower data

Abstract Information on gross primary production (GPP) of maize croplands is needed for assessing and monitoring maize crop conditions and the carbon cycle. A number of studies have used the eddy covariance technique to measure net ecosystem exchange (NEE) of CO2 between maize cropland fields and the atmosphere and partitioned NEE data to estimate seasonal dynamics and interannual variation of GPP in maize fields having various crop rotation systems and different water management practices. How to scale up in situ observations from flux tower sites to regional and global scales is a challenging task. In this study, the Vegetation Photosynthesis Model (VPM) and satellite images from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to estimate seasonal dynamics and interannual variation of GPP during 2001–2005 at five maize cropland sites located in Nebraska and Minnesota of the U.S.A. These sites have different crop rotation systems (continuously maize vs. maize and soybean rotated annually) and different water management practices (irrigation vs. rain-fed). The VPM is based on the concept of light absorption by chlorophyll and is driven by the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI), photosynthetically active radiation (PAR), and air temperature. The seasonal dynamics of GPP predicted by the VPM agreed well with GPP estimates from eddy covariance flux tower data over the period of 2001–2005. These simulation results clearly demonstrate the potential of the VPM to scale-up GPP estimation of maize cropland, which is relevant to food, biofuel, and feedstock production, as well as food and energy security

Modeling gross primary production of irrigated and rain-fed maize using MODIS imagery and CO\u3csub\u3e2\u3c/sub\u3e flux tower data

Abstract Information on gross primary production (GPP) of maize croplands is needed for assessing and monitoring maize crop conditions and the carbon cycle. A number of studies have used the eddy covariance technique to measure net ecosystem exchange (NEE) of CO2 between maize cropland fields and the atmosphere and partitioned NEE data to estimate seasonal dynamics and interannual variation of GPP in maize fields having various crop rotation systems and different water management practices. How to scale up in situ observations from flux tower sites to regional and global scales is a challenging task. In this study, the Vegetation Photosynthesis Model (VPM) and satellite images from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to estimate seasonal dynamics and interannual variation of GPP during 2001–2005 at five maize cropland sites located in Nebraska and Minnesota of the U.S.A. These sites have different crop rotation systems (continuously maize vs. maize and soybean rotated annually) and different water management practices (irrigation vs. rain-fed). The VPM is based on the concept of light absorption by chlorophyll and is driven by the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI), photosynthetically active radiation (PAR), and air temperature. The seasonal dynamics of GPP predicted by the VPM agreed well with GPP estimates from eddy covariance flux tower data over the period of 2001–2005. These simulation results clearly demonstrate the potential of the VPM to scale-up GPP estimation of maize cropland, which is relevant to food, biofuel, and feedstock production, as well as food and energy security