Ted Huffman

Impact of climate change scenarios on Canadian agroclimatic indices

Qian, B., De Jong, R., Gameda, S., Huffman, T., Neilsen, D., Desjardins, R., Wang, H. and McConkey, B. 2013. Impact of climate change scenarios on Canadian agroclimatic indices. Can. J. Soil Sci. 93: 243-259. The Canadian agricultural sector is facing the impacts of climate change. Future scenarios of agroclimatic change provide information for assessing climate change impacts and developing adaptation strategies. The goal of this study was to derive and compare agroclimatic indices based on current and projected future climate scenarios and to discuss the potential implications of climate change impacts on agricultural production and adaptation strategies in Canada. Downscaled daily climate scenarios, including maximum and minimum temperatures and precipitation for a future time period, 2040-2069, were generated using the stochastic weather generator AAFC-WG for Canadian agricultural regions on a 0.5°×0.5° grid. Multiple climate scenarios were developed, based on the results of climate change simulations conducted using two global climate models - CGCM3 and HadGEM1 - forced by IPCC SRES greenhouse gas (GHG) emission scenarios A2, A1B and B1, as well as two regional climate models forced by the A2 emission scenario. The agroclimatic indices that estimate growing season start, end and length, as well as heat accumulations and moisture conditions during the growing season for three types of field crops, cool season, warm season and over-wintering crops, were used to represent agroclimatic conditions. Compared with the baseline period 1961-1990, growing seasons were projected to start earlier, on average 13 d earlier for cool season and over-wintering crops and 11 d earlier for warm season crops. The end of the growing season was projected on average to be 10 and 13 d later for over-wintering and warm season crops, respectively, but 11 d earlier for cool season crops because of the projected high summer temperatures. Two indices quantifying the heat accumulation during the growing season, effective growing degree days (EGDD) and crop heat units (CHU) indicated a notable increase in heat accumulation: on average, EGDD increased by 15, 55 and 34% for cool season, warm season and over-wintering crops, respectively. The magnitudes of the projected changes were highly dependent on the climate models, as well as on the GHG emission scenarios. Some contradictory projections were observed for moisture conditions based on precipitation deficit accumulated over the growing season. This confirmed that the uncertainties in climate projections were large, especially those related to precipitation, and such uncertainties should be taken into account in decision making when adaptation strategies are developed. Nevertheless, the projected changes in indices related to temperature were fairly consistent.

Estimating plant area index for monitoring crop growth dynamics using Landsat-8 and RapidEye images

Abstract This study investigates the use of two different optical sensors, the multispectral imager (MSI) onboard the RapidEye satellites and the operational land imager (OLI) onboard the Landsat-8 for mapping within-field variability of crop growth conditions and tracking the seasonal growth dynamics. The study was carried out in southern Ontario, Canada, during the 2013 growing season for three annual crops, corn, soybeans, and winter wheat. Plant area index (PAI) was measured at different growth stages using digital hemispherical photography at two corn fields, two winter wheat fields, and two soybean fields. Comparison between several conventional vegetation indices derived from concurrently acquired image data by the two sensors showed a good agreement. The two-band enhanced vegetation index (EVI2) and the normalized difference vegetation index (NDVI) were derived from the surface reflectance of the two sensors. The study showed that EVI2 was more resistant to saturation at high biomass range than NDVI. A linear relationship could be used for crop green effective PAI estimation from EVI2, with a coefficient of determination ( R 2 ) of 0.85 and root-mean-square error of 0.53. The estimated multitemporal product of green PAI was found to be able to capture the seasonal dynamics of the three crops.

Upscaling modelled crop yields to regional scale: A case study using DSSAT for spring wheat on the Canadian prairies

Huffman, T., Qian, B., De Jong, R., Liu, J., Wang, H., McConkey, B., Brierley, T. and Yang, J. 2015. Upscaling modelled crop yields to regional scale: A case study using DSSAT for spring wheat on the Canadian prairies. Can. J. Soil Sci. 95: 49–61. Dynamic crop models are often operated at the plot or field scale. Upscaling is necessary when the process-based crop models are used for regional applications, such as forecasting regional crop yields and assessing climate change impacts on regional crop productivity. Dynamic crop models often require detailed input data for climate, soil and crop management; thus, their reliability may decrease at the regional scale as the uncertainty of simulation results might increase due to uncertainties in the input data. In this study, we modelled spring wheat yields at the level of numerous individual soils using the CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) and then aggregated the simulated yields from individual soils to regions where crop yields were reported. A comparison between the aggregated and the reported yields was performed to examine the potential of using dynamic crop models with individual soils in a region for the simulation of regional crop yields. The regionally aggregated simulated yields demonstrated reasonable agreement with the reported data, with a correlation coefficient of 0.71 and a root-mean-square error of 266 kg ha-1 (i.e., 15% of the average yield) over 40 regions on the Canadian prairies. Our conclusion is that aggregating simulated crop yields on individual soils with a crop model can be reliable for the estimation of regional crop yields. This demonstrated its potential as a useful approach for using crop models to assess climate change impacts on regional crop productivity.

Modified vegetation indices for estimating crop fraction of absorbed photosynthetically active radiation

The fraction of absorbed photosynthetically active radiation (FPAR) is an important biophysical parameter of vegetation. It is often estimated using vegetation indices (VIs) derived from remote-sensing data, such as the normalized difference VI (NDVI). Ideally a linear relationship is used for the estimation; however, most conventional VIs are affected by canopy background reflectance and their sensitivity to FPAR declines at high biomass. In this study, a multiplier, the ratio of the green to the red reflectance, was introduced to improve the linear relationship between VIs and crop FPAR. Three widely used VIs – NDVI, the green normalized difference VI (GNDVI), and the renormalized difference VI (RDVI) – were modified this way and were called modified NDVI (MNDVI), modified GNDVI (MGNDVI), and modified RDVI (MRDVI), respectively. A sensitivity study was applied to analyse the correlation between the three modified indices and the leaf area index (LAI) using the reflectance data simulated by the combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model (PROSAIL model). The results revealed that these new indices reduced the saturation trend at high LAI and achieved better linearity with crop LAI at low-to-medium biomass when compared with their corresponding original versions. This has also been validated using in situ FPAR measurements over wheat and maize crops. In particular, estimation using MNDVI achieved a coefficient of determination (R2) of 0.97 for wheat and 0.86 for maize compared to 0.90 and 0.82 for NDVI, respectively, while MGNDVI achieved 0.97 for wheat and 0.88 for maize, compared to 0.90 and 0.81 for GNDVI, respectively. Algorithms based on the VIs when applied to both wheat and maize showed that MNDVI and MGNDVI achieved a better linearity relationship with FPAR (R2 = 0.92), in comparison with NDVI (R2 = 0.85) and GNDVI (R2 = 0.82). The study demonstrated that applying the green to red reflectance ratio can improve the accuracy of FPAR estimation.

Deriving Maximum Light Use Efficiency From Crop Growth Model and Satellite Data to Improve Crop Biomass Estimation

Maximum light use efficiency (LUEmax) is an important parameter in biomass estimation models (e.g., the Production Efficiency Models (PEM)) based on remote sensing data; however, it is usually treated as a constant for a specific plant species, leading to large errors in vegetation productivity estimation. This study evaluates the feasibility of deriving spatially variable crop LUEmax from satellite remote sensing data. LUEmax at the plot level was retrieved first by assimilating field measured green leaf area index and biomass into a crop model (the Simple Algorithm for Yield estimate model), and was then correlated with a few Landsat-8 vegetation indices (VIs) to develop regression models. LUEmax was then mapped using the best regression model from a VI. The influence factors on LUEmax variability were also assessed. Contrary to a fixed LUEmax, our results suggest that LUEmax is affected by environmental stresses, such as leaf nitrogen deficiency. The strong correlation between the plot-level LUEmax and VIs, particularly the two-band enhanced vegetation index for winter wheat (Triticum aestivum) and the green chlorophyll index for maize (Zea mays) at the milk stage, provided a potential to derive LUEmax from remote sensing observations. To evaluate the quality of LUEmax derived from remote sensing data, biomass of winter wheat and maize was compared with that estimated using a PEM model with a constant LUEmax and the derived variable LUEmax. Significant improvements in biomass estimation accuracy were achieved (by about 15.0% for the normalized root-mean-square error) using the derived variable LUEmax . This study offers a new way to derive LUEmax for a specific PEM and to improve the accuracy of biomass estimation using remote sensing.

Assessing the Impact of Climate Variability on Cropland Productivity in the Canadian Prairies Using Time Series MODIS FAPAR

Cropland productivity is impacted by climate. Knowledge on spatial-temporal patterns of the impacts at the regional scale is extremely important for improving crop management under limiting climatic factors. The aim of this study was to investigate the effects of climate variability on cropland productivity in the Canadian Prairies between 2000 and 2013 based on time series of MODIS (Moderate Resolution Imaging Spectroradiometer) FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) product. Key phenological metrics, including the start (SOS) and end of growing season (EOS), and the cumulative FAPAR (CFAPAR) during the growing season (between SOS and EOS), were extracted and calculated from the FAPAR time series with the Parametric Double Hyperbolic Tangent (PDHT) method. The Mann-Kendall test was employed to assess the trends of cropland productivity and climatic variables, and partial correlation analysis was conducted to explore the potential links between climate variability and cropland productivity. An assessment using crop yield statistical data showed that CFAPAR can be taken as a surrogate of cropland productivity in the Canadian Prairies. Cropland productivity showed an increasing trend in most areas of Canadian Prairies, in general, during the period from 2000 to 2013. Interannual variability in cropland productivity on the Canadian Prairies was influenced positively by rainfall variation and negatively by mean air temperature.

Identifying Major Crop Types in Eastern Canada Using a Fuzzy Decision Tree Classifier and Phenological Indicators Derived from Time Series MODIS Data

Abstract. This article presents a methodology that uses a fuzzy decision tree classifier and phenological indicators derived from remote sensing data for identifying major crop types in southwestern Ontario in eastern Canada. Phenological indicators were derived from time series Normalized Difference Vegetation Index (NDVI) calculated from 250-m surface reflectance data of the Moderate Resolution Imaging Spectroradiometer (MODIS). Training and testing samples were derived from crop classification maps at 30-m resolution for 2011, 2012, and 2013. Training samples for 2013 were used for discrimination rule development, and the classifier was then applied to all 3 years. Results showed that the classifier was able to discriminate major crop types such as winter wheat, corn, soybean, and forage crops with an overall accuracy of 75.3 % for 2013 and comparable accuracy for 2011 and 2012. Confusion exists mainly between corn and soybean, and between winter wheat and forage crops. This indicates that phenological indicators derived from optical remote sensing data are intrinsic to a crop and might be more indicative than the commonly used remote sensing features that are susceptible to environmental and management impacts. This methodology provides an opportunity for discriminating general crop types without requiring a year-specific training sample set.

Sensitivity study of Radarsat-2 polarimetric SAR to crop height and fractional vegetation cover of corn and wheat

ABSTRACT Increasing studies have been conducted to investigate the potential of polarimetric synthetic aperture radar (SAR) in crop growth monitoring due to the capability of penetrating the clouds, haze, light rain, and vegetation canopy. This study investigated the sensitivity of 16 parameters derived from C-band Radarsat-2 polarimetric SAR data to crop height and fractional vegetation cover (FVC) of corn and wheat. The in-situ measured crop height and FVC were collected from 29 April to 30 September 2013, at the study site in southwest Ontario, Canada. A total of 10 Radarsat-2 polarimetric SAR images were acquired throughout the same growing season. It was observed that at the early growing stage, the corn height was strongly correlated with the SAR parameters including HV (R2 = 0.88), HH-VV (R2 = 0.84), and HV/VV (R2 = 0.80), and the corn FVC was significantly correlated with HV (R2 = 0.79) and HV/VV (R2 = 0.92), but the correlation became weaker at the later growing stage. The sensitivity of the SAR parameters to wheat variables was very low and only HV and Yamaguchi helix scattering showed relatively good but negative correlations with wheat height (R2 = 0.57 and R2 = 0.39) at the middle growing stage. These findings indicated that Radarsat-2 polarimetric SAR (C-band) has a great potential in crop height and FVC estimation for broad-leaf crops, as well as identifying the changes in crop canopy structures and phenology.

Delineating managed land for reporting national greenhouse gas emissions and removals to the United Nations framework convention on climate change

Land use and management activities have a substantial impact on carbon stocks and associated greenhouse gas emissions and removals. However, it is challenging to discriminate between anthropogenic and non-anthropogenic sources and sinks from land. To address this problem, the Intergovernmental Panel on Climate Change developed a managed land proxy to determine which lands are contributing anthropogenic greenhouse gas emissions and removals. Governments report all emissions and removals from managed land to the United Nations Framework Convention on Climate Change based on this proxy, and policy interventions to reduce emissions from land use are expected to focus on managed lands. Our objective was to review the use of the managed land proxy, and summarize the criteria that governments have applied to classify land as managed and unmanaged. We found that the large majority of governments are not reporting on their application of the managed land proxy. Among the governments that do provide information, most have assigned all area in specific land uses as managed, while designating all remaining lands as unmanaged. This designation as managed land is intuitive for croplands and settlements, which would not exist without management interventions, but a portion of forest land, grassland, and wetlands may not be managed in a country. Consequently, Brazil, Canada and the United States have taken the concept further and delineated managed and unmanaged forest land, grassland and wetlands, using additional criteria such as functional use of the land and accessibility of the land to anthropogenic activity. The managed land proxy is imperfect because reported emissions from any area can include non-anthropogenic sources, such as natural disturbances. However, the managed land proxy does make reporting of GHG emissions and removals from land use more tractable and comparable by excluding fluxes from areas that are not directly influenced by anthropogenic activity. Moreover, application of the managed land proxy can be improved by incorporating additional criteria that allow for further discrimination between managed and unmanaged land.

Modelling soybean yield responses to seeding date under projected climate change scenarios

Abstract: Climate change is projected to increase growing season length and temperature in Canada but how soybean [Glycine max (L.) Merr.] will respond is uncertain. By modelling soybean responses to climate change scenarios, stakeholders can develop adaptation strategies. The CSM-CROPGRO-Soybean and STICS models were used to simulate soybean responses under baseline (1971–2000) and in near (2041–2070) and distant (2071–2100) future climate scenarios, including those resulting in altered seeding dates in eastern Canada. Field data collected in Ottawa were used to evaluate the models. The simulated seed yield using the CSM-CROPGRO-Soybean model showed an increase of about 14% (0.34 t ha-1) in the near future and a decrease in the distant future under RCP8.5 and the STICS model estimated a decrease in both the near and distant future. When the crop parameters determining the life cycle were increased by 30% and 40%, the simulated seed yield increased by more than 5%–10% and 10%–20% and by more than 20%–30% and 27%–40% if combined with current harvest index levels. Our simulations showed that soybean seed yield would not benefit from a prolonged growing season under the projected future climate in eastern Canada, unless harvest index is maintained.