Impact Factor 3.678

The world's most-cited Plant Sciences journal

Original Research ARTICLE

Front. Plant Sci., 17 November 2015 | https://doi.org/10.3389/fpls.2015.00990

Yield and Production Gaps in Rainfed Wheat, Barley, and Canola in Alberta

  • 1Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
  • 2Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada

Improving crop yields are essential to meet the increasing pressure of global food demands. The loss of high quality land, the slowing in annual yield increases of major cereals, increasing fertilizer use, and the effect of this on the environment all indicate that we need to develop new strategies to increase grain yields with less impact on the environment. One strategy that could help address this concern is by narrowing the yield gaps of major crops using improved genetics and management. The objective of this study was to determine wheat (Triticum spp. L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.) yields and production gaps in Alberta. We used 10 years of data (2005–2014) to understand yield variability and input efficiency at a farmers’ specified level of management, and the yield potential under optimal management to suggest appropriate pathways for closing yield gaps. Significant management gaps were observed between attainable and actual yields of rainfed wheat (24%), barley (25%), and canola (30%). In addition, genetic gaps (i.e., gaps due to genetic selection) in wheat, barley, and canola were 18, 12, and 5%, respectively. Genetic selection with optimal crop management could increase yields of wheat, barley, and canola significantly, with estimated yield gains of 3.42, 1.92, and 1.65 million tons, respectively, each year under rainfed conditions in Alberta. This paper identifies yield gaps and offers suggestions to improve efficiency in crop production.

Introduction

Improving crop yields is essential to meet the increasing demand for food driven by the increasing population and income growth in the 21st century. One strategy that could address this concern is by quantifying the production capacity of farmland to identify ways to increase the yield of major crops (Patrignani et al., 2014). This can be achieved by using high yielding management practices (Yang et al., 2008), and closing yield gaps between farmers’ actual yield and potential yield (Cassman et al., 2003; Licker et al., 2010; Tilman et al., 2011; Mueller et al., 2012). Miminizing yield gaps in major crops by using optimal management practices may lead to improvements in production, while offering both environmental benefits and economic value. Assessing the yield gaps in major field crops can help us understand yield variability, yield potential, and the input efficiency of major crops and may indicate appropriate pathways for improving agricultural efficiencies (Fischer et al., 2009; Carberry et al., 2013; Van Ittersum et al., 2013).

Yield gap analysis uses data from field experiments, satellites, simulation models, or a combination of these to understand yield variability and potentiality (Lobell et al., 2005, 2009; Lobell, 2013; Van Ittersum et al., 2013). Recent studies in the US and internationally have shown that yield gaps exists in wheat (Triticum spp. L. – Fischer et al., 2009; Carberry et al., 2013; Patrignani et al., 2014), maize (Zea mays L. – Egli and Hatfield, 2014a; Rufo et al., 2015), rice (Oryza sativa L. – Roel and Plant, 2004; Yang et al., 2008), and soybean [Glycine max (L.) Merr. – Egli and Hatfield, 2014b]. Mueller et al. (2012) determined that for most major crops, 60–80% of global yield variability is related to climate, fertilizer application, and irrigation. This variation can be minimized by improving management practices that have the greatest influence on achieving crop yield potential (Dobermann et al., 2003). To date, no information is available to indicate whether the yield and production gaps exist in major field crops in Canada.

The production capacity of farmland is normally calculated as grain yield per unit area, at a standard moisture content. The starting point is actual farm yield (Yf), which is average yield achieved in a farmer’s field using the most widely accepted management practices such as sowing date, plant density, nutrient management, and crop protection (Fischer et al., 2009; Van Ittersum et al., 2013). Other measures to quantify these gaps are attainable yield (Ya), irrigated yield (Yi), and potential yield (Yp). Attainable yield is the crop yield grown under optimal management practices (i.e., recommended plant density, non-limiting nutrient condition, effective control of biotic stresses, etc.) in farmers’ fields (Van Ittersum et al., 2013). Ya, also referred to as water-limited yield, is the most relevant benchmark for rainfed crops (Evans, 1993; Van Ittersum and Rabbinge, 1997), where crops are limited by water supply and yield is influenced by soil type and field topography. Irrigated yield (Yi) represents the yield when a crop is grown under fully irrigated conditions or systems with ample rainfall. In irrigated systems, yield under optimum management is labeled as potential yield (Yp) when a crop is grown with nutrient and water non-limiting conditions and effectively controlled biotic stresses (Evans, 1993; Van Ittersum and Rabbinge, 1997). Yp assumes optimal management conditions and that crop growth is determined by genetic characteristics and climatic factors (e.g., solar radiation, temperature, atmospheric CO2, light, etc.) (Van Ittersum et al., 2013). The yield gap is primarily the difference between Ya (rainfed crops), or Yp (irrigated crops) and actual farm yields (Yf).

Wheat and barley (Hordeum vulgare L.) are the principal cereal grains and canola (primarily Brassica napus L.) the major oil crop, grown in Canada. Average total national production of spring wheat from 2005 to 2014 was 18.98 million tons (MMt) grown on 6.74 million ha, at an average of 2.82 t ha-1, while barley covered 2.95 million ha producing 9.44 MMt, or 3.23 t ha-1 (Statistics Canada, 2015). Canola was cultivated on 6.9 million ha with an average productivity of 1.86 t ha-1 over the same 10 years span (Statistics Canada, 2015). The proportion of these crops grown in Alberta, Canada are: wheat 34% (2.31 million ha), barley 46% (1.37 million ha), and canola 32% (2.2 million ha) (Statistics Canada, 2015). Although Alberta has the majority of irrigated land in Canada, most of the land is not irrigated.

Wheat, barley, and canola yields vary in Alberta due to genotype (cultivar), location, and management practices (Anbessa et al., 2009; O’Donovan et al., 2011; Harker et al., 2012). Anbessa et al. (2009) reported yields from 5.5 to 8.1 t ha-1 for 25 spring barley genotypes grown across six environments in Alberta while O’Donovan et al. (2011) reported yields of 4.1 to 4.4 t ha-1 for two malting barleys grown in eight environments. Canola has been a very popular crop among Alberta farmers with the average yield ranging between 2.1 and 4.8 t ha-1 (Harker et al., 2012), with the highest yields reported from central Alberta. However, quantification of yield variability of these crops/cultivars at a farmers’ specified level of management, and their potential under optimal management conditions has not been reported. This study was based on the assumption that there exists yield and production gaps in rainfed wheat, barley, and canola in Alberta and that there is a possibility of increasing crop yields by genetic selection and optimal crop management.

The objective of this study was to calculate crop yield and yield potential for the major field crops in Alberta, and to quantify yield gaps. We also identified the gaps that exist between the different measures of yield. Finally, we discuss possible means to narrow the existing gaps. This research offers the opportunity to improve the productivity and the profitability of the three major field crops, wheat, barley, and canola, in western Canada.

Materials And Methods

Agroclimatic Conditions in Study Sites

Data on soils and climatic conditions at the research sites were collected from the closest Environment Canada or Alberta Agriculture research station within 5 km of test plots. Soil types and average seasonal precipitation over a period of 10 years are shown in Table 1. The five soil types observed in the study locations were Gray Luvisol, Brown Chernozem, Dark Brown Chernozem, Dark Gray Chernozem, and Black Chernozem with the seasonal precipitation received ranging between 197 mm (Acadia Valley) and 288 mm (Lacombe) during the cropping season from May to August.

TABLE 1
www.frontiersin.org

TABLE 1. Soil types and seasonal precipitation in the study locations in Alberta.

Yield and Yield Gap Analysis

The agronomic analysis for this study was based on 18 wheat, 20 barley, and 22 canola genotypes tested at 21 locations across north, south, and central Alberta over a period of 10 years (2005–2014) (Table 2). Genotypes were selected for inclusion based on the area planted. The cultivars selected included all those that occupy >1% (10-year’s average) of the total cultivated area (Agriculture Financial Services Corporation [AFSC], 2015).

TABLE 2
www.frontiersin.org

TABLE 2. Cultivar performance at farmer’s specified level of management under rainfed condition at 21 locations across Alberta over a period of 10 years (2005–2014) (Agriculture Financial Services Corporation [AFSC], 2015).

Actual farm yield (Yf) and irrigated yields (Yi) at provincial and regional levels were determined from Agriculture Financial Services Corporation [AFSC] (2015) and Statistics Canada (2015), while the attainable (Ya) and maximum attainable (Ym) yields of wheat, barley, and canola were derived from the farmers’ managed crop variety performance trials in the same areas (Alberta Regional Variety Trials – Alberta Agriculture and Rural Development [AARD], 2014 and data courtesy of Dean Spaner, Department of Agricultural, Food, and Nutritional Science, University of Alberta) that used optimal crop and nutrient management practices (e.g., soil testing and application of nutrients based on soil types and crop demand, appropriate planting density, effective control of biotic stresses, etc.). Actual, attainable, maximum attainable, and irrigated yields were calculated as described in Table 3. The gap between the attainable and actual yields (Ya–Yf) is a measure of the benefit of proper crop management (i.e., management gap) which would include the proper use of fertilizers and crop protection measures. The gap between the maximum attainable and attainable yields (Ym–Ya) is a measure of the benefit of using the optimal crop variety (i.e., genetic gap) for that region, while the difference between the maximum attainable and actual yields (Ym–Yf) is a measure of the benefit of proper crop management plus optimal variety (i.e., total gap), together. Similarly, the gap between the irrigated and actual yields (Yi–Yf) is a measure of adequate moisture for the crop (i.e., moisture gap), and is often measured by the difference between irrigated and non-irrigated crops. The highest yield (Yh) indicates record wheat, barley, and canola yields observed during study period.

TABLE 3
www.frontiersin.org

TABLE 3. Yield metrics and yield gap calculation in wheat, barley, and canola.

Statistical Analyses

An analysis of variance (ANOVA) for grain yield as determined by the genotype, year, and their interactions was done for each location separately using a randomized complete block design. A combined ANOVA was also done from the mean data from each location, to create the means data for the different statistical analyses. The effects of the genotype, location, and year as well as their first and second order interactions were determined from the ANOVA analysis. Genotypes were assumed to be fixed, and year and location effects random. The software package, Agrobase TM (1990, Agronomix Software Inc.), was used for statistical analyses. Bartlett’s (1947) test was used to determine the homogeneity of variances between environments to determine the validity of the combined ANOVA on the data.

Results

Actual, attainable, maximum attainable, irrigated, and the highest yield recorded for rainfed wheat, barley, and canola in Alberta are shown in Table 4, along with the wheat yield data from Australia, China, and the UK. The 10-years average actual yields (Yf) of rainfed wheat, barley, and canola achieved by farmers in Alberta were 3.20, 3.46, and 2.06 t ha-1, respectively (Statistics Canada, 2015). Similarly, average attainable yields (Ya) were 3.96, 4.32, and 2.68 t ha-1, for wheat, barley, and canola, respectively. Therefore, significant management gaps were observed due to difference between actual (Yf) and attainable yields (Ya) of wheat (an increase of 0.76 t ha-1, 24%), barley (0.86 t ha-1, i.e., 25%), and canola (0.62 t ha-1, i.e., 30%) under rainfed conditions (Table 4).

TABLE 4
www.frontiersin.org

TABLE 4. Actual, attainable, maximum attainable, and irrigated yields of wheat, barley, and canola over a period of 10 years (2005–2014) in Alberta.

The maximum attainable yields of rainfed wheat, barley, and canola were 4.68, 4.86, and 2.81 t ha-1, respectively, with the average genetic gaps (i.e., the gap between attainable (Ya) and maximum attainable (Ym) yields) of 18% (an increase of 0.72 t ha-1) in wheat, 12% (an increase of 0.54 t ha-1) in barley, and 5% (an increase of 0.13 t ha-1) in canola (Table 4). Similarly, the total gaps (i.e., the gap between actual (Yf) and maximum attainable (Ym) yields) were 46, 40, and 36% indicating that combination of optimal management practices and genetic selection can increase grain yields up to 4.68, 4.86, and 2.81 t ha-1 for rainfed wheat, barley, and canola, respectively, in Alberta (Table 4). The average irrigated yields of wheat, barley, and canola were 4.74, 4.57, and 2.77 t ha-1, showing moisture gaps (i.e., the gap between the irrigated yield and the actual farm yield) of 48, 32, and 35%, respectively.

Table 5 provides an ANOVA for grain yield as determined by the genotype, year, and location as well as their first and second order interactions. Significant variation was observed in the yield of wheat, barley, and canola between genotypes and location under optimal nutrient management. The largest variation was seen between locations (CV = 21.3%), probably due to differences in precipitation and soil type (Table 1).

TABLE 5
www.frontiersin.org

TABLE 5. Estimates of variance components for grain yield, genotypes, and their interactions with location and years under optimal nutrient management.

Table 6 lists the average actual, attainable, and maximum attainable yields of rainfed wheat, barley, and canola and the percent gap between these components, according to farm location in Alberta. Management gaps (Ya–Yf) ranged between 12–40, 7–39, and 15–42% whereas the genetic gaps (Ym–Ya) ranged between 10–32, 11–17, and 4–5% in rainfed wheat, barley, and canola, respectively. The highest yield was observed in areas which receive sufficient rainfall during the cropping season (Tables 1 and 6). There was a positive association (r2 = 0.45) between grain yield and total amount of precipitation over cropping seasons (May to August), showing that location is an another factor in determining grain yield in addition to genotype. Dark Brown to Black Chernozem soils in Lacombe, Stony Plain, Ft. Kent, and Neapolis produced significantly higher yield under optimal management compared to other locations and showed higher gaps in yield (Table 6).

TABLE 6
www.frontiersin.org

TABLE 6. Average actual, attainable, and maximum attainable yields of rainfed wheat, barley, and canola and the percent gap between these components by location in Alberta over a period of 10 years (2005–2014) (Alberta Agriculture and Rural Development [AARD], 2014; Agriculture Financial Services Corporation [AFSC], 2015).

Variation also existed among wheat, barley, and canola genotypes selected for yield gaps studies. The rank-wise mean yield of the 18 wheat, 20 barley, and 22 canola genotypes evaluated at 21 sites across Aberta from 2005 to 2014 is given in Table 2. The cultivars with the lowest coefficient of variation across the years and locations were, for wheat: Superb, Harvest, Radiant, Stettler, and CDC Abound, for barley: CDC Meredith, Champion, Xena, CDC Coalition, and Sundre, and for canola: L130, Invigor 5440, VR 9559G, and 45H29.

Table 7 shows the gains in yield that are possible by minimizing yield gaps in wheat, barley, and canola in Alberta alone. Based on this study, the estimated gain in yields of wheat, barley, and canola due to optimal crop management (i.e., management gain) is 1.76, 1.18, and 1.36 million tons, respectively, which was worth $395M, $183M, and $466M (USD) annually, based on 2014–2015 cumulative average crop prices. Production gains which combined genetic selection (i.e., selection of appropriate cultivars) together with an optimal crop management were found to be 3.42, 1.92, and 1.65 million tons of wheat, barley, and canola anually which is equivalent to $769M, $297M, and $564M (USD), respectively (Table 7). In other words, the cost of poor genetics (i.e., selection of inappropriate cultivars) was found to be $374M (1.66 million tons) in wheat, $115M (0.74 million tons) in barley, and $98M (0.29 million tons) in canola in Alberta.

TABLE 7
www.frontiersin.org

TABLE 7. Estimated yield gain in wheat, barley, and canola due to improved crop management and genetic selection under rainfed condition in Alberta.

Discussion

The analysis of yield gaps is important to identify the potential sources of gains in agricultural yields and to develop solutions to reduce these gaps. These solutions can increase crop yields and optimize the use of applied agricultural inputs. Our studies identified exploitable yield gaps between actual yield (Yf), water limited or attainable yield (Ya), maximum attainable yield (Ym), and irrigated yields (Yi) in wheat, barley, and canola in Alberta.

Actual yield varied between locations and years depending on soil type, rainfall, and climate, but was often lower than attainable yield due to poor crop and nutrient management practices. The management gap between Yf and Ya can be narrowed by using site specific crop and nutrient management practices (i.e., soil testing and use of the right amount of fertilizer, planting date, and density, and effective control of biotic stresses) for the choosen cultivars, whereas, the genetic gap between Ya and Ym represents the difference between the top performing variety in that location and the varieties that were chosen. Therefore, the genetic gap can be reduced by selecting the appropriate cultivar, and these gains would continue as new and improved varieties became available. The total gap between Yf and Ym represents the genetic and management gains that can be made using better performing cultivars under optimal management condition and can be minimized when several constraints (e.g., selection of high yielding cultivars, site specific crop and nutrient management practices) are addressed simultaneously. Minimizing this total gap has the potential to offer significant improvements in yield (4.68, 4.86, and 2.81 t ha-1 of wheat, barley, and canola, respectively) under rainfed conditions in Alberta.

The moisture gap between Yf and Yi can be narrowed by irrigation for the choosen cultivars. In rainfed system, this gap is mainly determined by factors that are difficult to control including the variation in climatic conditions. The moisture gap, therefore, is usually smaller in seasons with very favorable weather conditions (Pasuquin and Witt, 2007) since adequate moisture allows for the greater utilization of available nutrients, CO2, radiation, and temperature, throughout the cropping season.

Yield gaps provide important guidance in the identification of these constraints. If the gaps are large despite using improved management practices, maximum/attainable yields must be limited by an unknown constraint (Pasuquin and Witt, 2007). If the gaps are small, it is usually not economical to aim to fully reducing the gap because of the large amounts of inputs required and the high risk of crop failure and economic losses. A comparative study of yield gaps in wheat in Australia and China suggested that there is only a small gap between actual and attainable yield in Australia while the gap is much wider (32%) in China (Carberry et al., 2013). The small yield gap in Australia, which was determined as reasonable, given the economics and risks, and was attributed to better infrastructure, agricultural institutions, and modernized farms (Carberry et al., 2013). Although the wheat yield gap is still approximately 32% in China, targeted breeding and the availability of higher water and/or N resources have resulted in closer gaps (Carberry et al., 2013) compared to other developing countries.

Yield variation exists among wheat, barley, and canola genotypes and between locations in Alberta (Anbessa et al., 2009; O’Donovan et al., 2011; Harker et al., 2012). Variation between locations is due to crop management, soil types, climate, and availability of moisture during cropping season. The management practices of the actual farm yield in the Alberta Prairies mainly constitutes large-scale production of a few genotypes with effective chemical weed control, higher soil disturbance due to removal of crop biomass after harvest, and reliance on synthetic nutrient formulations which can result in nutrient deficiencies in cropping systems (Martens et al., 2013). Only 20% of the fields in Alberta have been sampled with soil tests, and many of those were only sampled every 3 years (International Plant Nutrition Institute [IPNI], 2010). In general, growers apply fertilizers based on reasons other than available soil N, such as trying to hit yield targets, using past personal experience or what other farmers have applied in their area (Heard, 2011). An unpredictable moisture regime also means than the additional cost of higher inputs may not be rewarded with increased yields.

Yield variation related to soil conditions, fertilizer application, the incorrect genotype, and irrigation can be minimized by adopting site specific optimal crop and nutrient management strategies, which could maximize economic returns. Soil testing, nutrient management planning, and minimum tillage/zero-till have been considered to be the top-performing best management practices (BMPs) in Alberta that can increase expected net revenue by 19, 33, and 35%, respectively, compared to the base model in brown soil zone (Crop Nutrients Council, 2007). Testing soil and manure for nutrient content, the balanced application of manures and mineral fertilizers, adoption of direct seeding, and the application of N fertilizers in bands or placing them safe in seed rows in spring instead of pre-seed broadcasting are several ways to offset costs due to excess applications and may improve yields and nitrogen use efficiency (Alberta Agriculture and Rural Development [AARD], 2015). In recent years, a large percentage of Albertan farmers adopted a minimum or no-tillage system. Producers using minimum tillage, however, identified fewer increases in yields compared to the tillage system, although they typically had improvements in expected net revenue due to reductions in operating costs (Crop Nutrients Council, 2007). Plant population is another factor that limits the crop yield potential of a given environment (Lobell et al., 2009); increased plant population only reduced the yield gap when all other inputs were applied at the supplemental level (Rufo et al., 2015).

Given that high quality agricultural land is being lost to development and producers are moving on to marginal lands for crop production, it is unlikely that more high quality land will be available in the future (Grassini et al., 2013). Furthermore, cereal crop yields, including wheat and barley, have slowed to a growth rate of about 1% annually since the 1990’s, and in some cases, specifically in developed countries, growth of crop yields is close to zero (Fischer et al., 2009). This study has shown that in Alberta, compared to current production levels, gains of 46, 40, and 36% in wheat, barley, and canola, respectively, could be achieved by using more appropriate genetic and management approaches for rainfed farming, leading to a large yield gain and a reduction in production costs.

Conclusion

This study assessed the yield gaps of the major field crops in Alberta (wheat, barley, and canola) over a period of 10 years (2005–2014), and revealed the possibility of improving yields of the existing cultivars of wheat, barley, and canola by 24, 25, and 30%, respectively, by using proper crop management (i.e., soil testing and use of right amount of fertilizer at right time and place, planting density, and pests and disease management). Average attainable yields from the existing cultivars of wheat, barley, and canola were 3.96, 4.32, and 2.68 t ha-1, respectively. Variation was also observed among the genotypes in each location which offers the opportunity of cultivar selection. The combination of optimal crop management practices and selection of location specific cultivars could increase grain yields up to 4.68 t ha-1 (46% higher than actual wheat yield), 4.86 t ha-1 (40% higher than actual barley yield), and 2.81 t ha-1 (36% higher than actual canola yield). This might lead to estimated yield gains of 3.42, 1.92, and 1.65 million tons of wheat, barley, and canola each year worth $769M, $297M, and $564M (USD), respectively, in Alberta.

Author Contributions

Author TC designed the study, managed the literature review, performed statistical analyses, and produced the first draft of the manuscript. Author AG provided supervision and support in all phases of this research. Both authors have read and approved the final manuscript.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank the collaborating farmers and Institutions (University of Alberta, FCDC, and AAFC, Lacombe, AFSC, Environment Canada, and Alberta Wheat Commission, Alberta Barley Commission, and the Canola Council of Canada) for providing information, support and data. Funding was provided by the Alberta Crop Industry Development Fund (ACIDF) and the Alberta Livestock and Meat Association (ALMA).

References

Agriculture Financial Services Corporation [AFSC] (2015). Yield Alberta: Crop Insurance Yields by Variety and Risk Areas [Online]. Available at: http://www.afsc.ca/doc.aspx?id=7883 (accessed June 17, 2015).

Agrobase TM (1990). Agronomix Software. Winnipeg, MB: Agronomic Software Inc.

Alberta Agriculture and Rural Development [AARD] (2014). Alberta Regional Variety Trials –2005-14 [Online]. Available at: http://www1.agric.gov.ab.ca/\$department/deptdocs.nsf/all/webdoc12969 (accessed December 22, 2014).

Alberta Agriculture and Rural Development [AARD] (2015). Wheat Nutrition and Fertilizer Requirements: Nitrogen [Online]. Available at: http://www1.agric.gov.ab.ca/\$department/deptdocs.nsf/all/webdoc12969 (accessed December 22, 2014).

Anbessa, Y., Juskiw, P., Good, A., Nyachiro, J., and Helm, J. (2009). Genetic variability in nitrogen use efficiency of spring barley. Crop Sci. 49, 1259–1269. doi: 10.2135/cropsci2008.09.0566

CrossRef Full Text | Google Scholar

Bartlett, M. S. (1947). The use of transformations. Biometrics 2, 39–52. doi: 10.2307/3001536

CrossRef Full Text | Google Scholar

Carberry, P. S., Liang, W., Twomlow, S., Holzworth, D. P., Dimes, J. P., McClelland, T., et al. (2013). Scope for improved eco-efficiency varies among diverse cropping systems. Proc. Natl. Acad. Sci. U.S.A. 110, 8381–8386. doi: 10.1073/pnas.1208050110

PubMed Abstract | CrossRef Full Text | Google Scholar

Cassman, K. G., Dobermann, A., Walters, D. T., and Yang, H. (2003). Meeting cereal demand while protecting natural resources and improving environmental quality. Annu. Rev. Environ. Resour. 28, 315–358. doi: 10.1146/annurev.energy.28.040202.122858

CrossRef Full Text | Google Scholar

Crop Nutrients Council (2007). An Economic Evaluation of Beneficial Management Practices for Crop Nutrients in Canadian Agriculture. Guelph, ON: George Morris Centre.

Google Scholar

Dobermann, A. R., Arkebauer, T. J., Cassman, K. G., Dribjer, R. A., and Lindquist, J. L. (2003). Understanding Corn Yield Potential in Different Environments [Online]. Available at: http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1524\&context=agronomyfacpub (accessed June 04, 2015).

Google Scholar

Egli, D. B., and Hatfield, J. L. (2014a). Yield and yield gaps in central US corn production systems. Agron. J. 106, 2248–2254. doi: 10.2134/agronj14.0348

CrossRef Full Text | Google Scholar

Egli, D. B., and Hatfield, J. L. (2014b). Yield gaps and yield relationships in central US soybean production systems. Agron. J. 106, 560–566. doi: 10.2134/agronj2003.0364

CrossRef Full Text | Google Scholar

Evans, L. T. (1993). Crop Evolution, Adaptation and Yield. Cambridge: Cambridge University Press.

Google Scholar

Farmers Weekly (2014). Brothers Battle for Top World Wheat Yields [Online]. Available at: http://www.fwi.co.uk/articles/28/04/2014/144315/brothers-battle-for-top-world-wheat-yields.htm (accessed October 22, 2014).

Fischer, R. A., Byerlee, D., and Edmeades, G. O. (2009). Can Technology Deliver on the Yield Challenge to 2050? Expert Meeting on How to feed the World in 2050. Rome: Food and Agriculture Organization of the United Nations, Economic and Social Development Department.

Google Scholar

Grassini, P., Eskridge, K. M., and Cassman, K. G. (2013). Distinguishing between yield advances and yield plateaus in historical crop production trends. Nat. Commun. 4, 2918. doi: 10.1038/ncomms3918

PubMed Abstract | CrossRef Full Text | Google Scholar

Harker, K. N., O’Donovan, J. T., Turkington, T. K., Blackshaw, R. E., Lupwayi, N. Z., Smith, E. G., et al. (2012). High-yield no-till canola production on the Canadian prairies. Can. J. Plant Sci. 92, 221–233. doi: 10.4141/cjps2011-125

CrossRef Full Text | Google Scholar

Heard, J. (2011). Manitoba Leads in Soil Testing, Precision Ag. [Online]. Available at: http://www.producer.com/2011/05/manitoba-leads-in-soil-testing-precision-ag/ (accessed May 28, 2015).

Hou, Y., Hou, G., Qi, H., Chen, C., Li, X., Zhao, M., et al. (2012). Analysis of photothermic resource use efficiency and potential increases in crop yields in high-yielding regions of eastern Asia. Aust. J. Crop Sci. 6, 784–792.

Google Scholar

International Plant Nutrition Institute [IPNI] (2010). Soil Test Levels in North America [Online]. Available at: http://info.ipni.net/soiltestsummary (accessed May 28, 2014).

Licker, R., Johnston, M., Foley, J. A., Barford, C., Kucharik, C. J., Monfreda, C., et al. (2010). Mind the gap: how do climate and agricultural management explain the ‘yield gap’ of croplands around the world? Glob. Ecol. Biogeogr. 19, 769–782. doi: 10.1111/j.1466-8238.2010.00563.x

CrossRef Full Text | Google Scholar

Lobell, D. B. (2013). The use of satellite data for crop yield gap analysis. Field Crop Res. 143, 56–64. doi: 10.1016/j.fcr.2012.08.008

CrossRef Full Text | Google Scholar

Lobell, D. B., Cassman, K. G., and Field, C. B. (2009). Crop yield gaps: their importance, magnitudes, and causes. Annu. Rev. Environ. Resour. 34, 179–204. doi: 10.1146/annurev.environ.041008.093740

CrossRef Full Text | Google Scholar

Lobell, D. B., Ortiz-Monasterio, J. I., Asner, G. P., Naylor, R. L., and Falcon, W. P. (2005). Combining field surveys, remote sensing, and regression trees to undertstand yield variations in an irrigated wheat landscape. Agron. J. 97, 241–249.

Google Scholar

Martens, J. T., Entz, M., and Wonneck, M. (2013). Ecological Farming Systems on the Canadian Prairies – A Path to Profitability, Sustainability and Resilience. A Report for the Science and Technology Branch of Agriculture and Agri-Food Canada. Winnipeg, MB: University of Manitoba, 1–4.

Mueller, N. D., Gerber, J. S., Johnston, M., Ray, D. K., Ramankutty, N., and Foley, J. A. (2012). Closing yield gaps through nutrient and water management. Nature 490, 254–257. doi: 10.1038/nature11420

PubMed Abstract | CrossRef Full Text | Google Scholar

O’Donovan, J. T., Turkington, T. K., Edney, M. J., Clayton, G. W., McKenzie, R. H., Juskiw, P. E., et al. (2011). Seeding rate, nitrogen rate and cultivar effects on malting barley production. Agron. J. 103, 709–716. doi: 10.1002/jsfa.5687

PubMed Abstract | CrossRef Full Text | Google Scholar

Pasuquin, J. M. C. A., and Witt, C. (2007). “Yield potential and yield gaps of maize in Southeast Asia,” in Proceedings of the International Potash Institute e-fic No. 14, IPI Southeast Asia Program, Singapore.

Patrignani, P., Lollato, R. P., Ochsner, T. E., Godsey, C. B., and Edwards, J. T. (2014). Yield gap and production gap of rainfed winter wheat in the Southern Grain Plains. Agron. J. 106, 1329–1339. doi: 10.2166/wst.2013.305

PubMed Abstract | CrossRef Full Text | Google Scholar

Roel, A., and Plant, R. E. (2004). Factors underlying yield variability in two California rice fields. Agron. J. 96, 1481–1494. doi: 10.2134/agronj2004.1481

CrossRef Full Text | Google Scholar

Rufo, M. L., Gentry, L. F., Henninger, A. S., Seebauer, J. R., and Below, F. E. (2015). Evaluating management factor contributions to reduce corn yield gaps. Agron. J. 107, 495–505. doi: 10.2134/agronj14.0355

CrossRef Full Text | Google Scholar

Statistics Canada (2015). Field and Special Crops [Online]. Available at: http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/prim11a-eng.htm (accessed January 14, 2015).

The Commonwealth Scientific and Industrial Research Organization [CSIRO] (2012). Record Wheat Yield Raises the Bar for Queensland Irrigators [Online]. Available at: http://www.csiro.au/Portals/Media/2012/Record-wheat-yield-achieved-in-QLD.aspx (accessed October 02, 2014).

Tilman, D., Balzer, C., Hill, J., and Befort, B. L. (2011). Global food demand and the sustainable intensification of agriculture. Proc. Natl. Acad. Sci. U.S.A. 108, 20260–20264. doi: 10.1073/pnas.1116437108

PubMed Abstract | CrossRef Full Text | Google Scholar

Van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, G., Tittonell, P., and Hochman, Z. (2013). Yield gap analysis with local to global relevance: a review. Field Crop Res. 143, 4–17. doi: 10.1016/j.fcr.2012.09.009

CrossRef Full Text | Google Scholar

Van Ittersum, M. K., and Rabbinge, R. (1997). Concepts in production ecology for analysis and quantification of agricultural input-output combinations. Field Crop Res. 52, 197–208. doi: 10.1016/S0378-4290(97)00037-3

CrossRef Full Text | Google Scholar

Yang, W., Peng, S., Laza, R. C., Visperas, R. M., and Dionisio-Sese, M. (2008). Yield gap analysis between dry and wet season rice crop grown under high-yielding management conditions. Agron. J. 100, 1390–1395. doi: 10.2134/agronj2007.0356

CrossRef Full Text | Google Scholar

Keywords: yield gaps, actual yield, attainable yield, maximum attainable yield, management gap, genetic gap, rainfed

Citation: Chapagain T and Good A (2015) Yield and production gaps in rainfed wheat, barley, and canola in Alberta. Front. Plant Sci. 6:990. doi: 10.3389/fpls.2015.00990

Received: 22 September 2015; Accepted: 29 October 2015;
Published: 17 November 2015.

Edited by:

Edmundo Acevedo, University of Chile, Chile

Reviewed by:

Paola Silva, University of Chile, Chile
Cándido López-Castañeda, Colegio de Postgraduados, Mexico

Copyright © 2015 Chapagain and Good. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Tejendra Chapagain, tejendra@uoguelph.ca