Liu, X., Burras, C. L., Kravchenko, Y. S., Duran, A., Huffman, T., Morras, H., Studdert, G., Zhang, X., Cruse, R. M. and Yuan, X. 2012. Overview of Mollisols in the world: Distribution, land use and management. Can. J. Soil Sci. 92: 383–402. Mollisols – a.k.a., Black Soils or Prairie Soils – make up about 916 million ha, which is 7% of the world's ice-free land surface. Their distribution strongly correlates with native prairie ecosystems, but is not limited to them. They are most prevalent in the mid-latitudes of North America, Eurasia, and South America. In North America, they cover 200 million ha of the United States, more than 40 million ha of Canada and 50 million ha of Mexico. Across Eurasia they cover around 450 million ha, extending from the western 148 million ha in southern Russia and 34 million ha in Ukraine to the eastern 35 million ha in northeast China. They are common to South America's Argentina and Uruguay, covering about 89 million and 13 million ha, respectively. Mollisols are often recognized as inherently productive and fertile soils. They are extensively and intensively farmed, and increasingly dedicated to cereals production, which needs significant inputs of fertilizers and tillage. Mollisols are also important soils in pasture, range and forage systems. Thus, it is not surprising that these soils are prone to soil erosion, dehumification (loss of stable aggregates and organic matter) and are suffering from anthropogenic soil acidity. Therefore, soil scientists from all of the world's Mollisols regions are concerned about the sustainability of some of current trends in land use and agricultural practices. These same scientists recommend increasing the acreage under minimum or restricted tillage, returning plant residues and adding organic amendments such as animal manure to maintain or increase soil organic matter content, and more systematic use of chemical amendments such as agricultural limestone to replenish soil calcium reserves.

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.

. 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: 49Á61. 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 CERESÁWheat 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.Key words: CERES-Wheat crop model, spring wheat, yield aggregation, regional application (Canada) Huffman, T., Qian, B., De Jong, R., Liu, J., Wang, H., McConkey, B., Brierley, T. et Yang, J. 2015. Mise a`l'e´chelle re´gionale des mode`les du rendement des cultures : e´tude de cas avec le DSSAT pour le ble´de printemps cultive´dans les Prairies canadiennes. Can. J. Soil Sci. 95: 49Á61. On recourt souvent aux mode`les agricoles dynamiques a`l'e´chelle de la parcelle ou du champ. Toutefois, une mise a`l'e´chelle s'impose quand on utilise les mode`les articule´s sur les processus pour des applications re´gionales comme la pre´vision du rendement des cultures ou l'e´valuation de l'impact du changement climatique sur la productivite´des cultures dans une re´gion. Les mode`les agricoles dynamiques ont souvent besoin de donne´es de´taille´es sur le climat, le sol et les pratiques agronomiques. C'est pourquoi leur fiabilite´diminue a`l'e´chelon re´gional, l'incertitude des re´sultats de la simulation pouvant s'accroıˆtre avec les incertitudes associe´es aux donne´es saisies. Dans le cadre de cette e´tude, les auteurs ont mode´lise´le rendement du ble´de printemps au niveau de nombreux sols individuels en recourant au mode`le CERES du ble´dans le syste`me d'aide a`la prise de de´cisions pour le transfert des technologies agricoles (...