Climate change impacts on hard red spring wheat yield and production risk: evidence from Manitoba, Canada

Abstract

A Just–Pope production function is employed to investigate the effects of historic weather changes on hard red spring wheat yield variability in Manitoba. Field-level data on wheat yield, proportion of wheat seeded area, and fertilizer inputs from the Manitoba Agricultural Services Corporation were employed to determine how temperature and precipitation affect mean wheat yield and production risk, and how projected climate scenarios impact yield variability in heterogeneous risk areas of Manitoba. Variety richness increases average yield and reduces yield variance while varieties protected by plant breeders’ rights increase yield variance. Phosphorus fertilizer is positively associated with average wheat yield while total precipitation is shown to negatively affect mean yield and positively impact yield variability. June precipitation matters while June and July temperatures negatively affect yield. Projected climate change is expected to increase yield variability in both the medium (2034–2050) and long term (2079–2095), both under low- and high-carbon scenarios with production variance effects differing across crop districts. Adaptation strategies may be required to mitigate yield risk effects of climate change resulting in late seeding decisions from increased spring precipitation.

Heterogeneous impacts of climate change on crop yields across altitudes in Ethiopia

“…However, the negative sign of the coefficient of quadratic term indicates that yield starts to decrease after a certain threshold of the temperature. This result is consistent with the findings of Carew (2017) who reported the negative effect of temperature exceeding 34° on the mean yield of wheat crop yield in Canada. In addition, precipitation has a U-shaped relationship with the mean yield of maize at high altitudes whereas its relationship with the mean yield of sorghum becomes inverted U-shaped at low altitudes.…”

“…(1) are the parameters of the model. As a common practice in the J-P literature (see, e.g., Carew 2017;Carew et al 2009;Chen et al 2004;Isik and Devadoss 2006;McCarl et al 2008; In line with Eq. ( 1), we specify the panel data regression model, indicated in Eq.…”

Historic climate change trends and impacts on crop yields in key agricultural areas of the prairie provinces in Canada: a literature review

“…For example, a 10% increase in September GDD resulted in a 2.8% increase in canola yield. On the contrary, in Manitoba, An and Carew (2015) determined that May GDD had a negative effect on both canola and barley yield but increasing the number of GDD in June and July reduced average spring wheat yields (Carew et al 2017). In southern Alberta, small canola yield increases of 0.16% occurred when GDD were increased by 1% for dryland crops, whereas smaller but nonsignificant increases occurred for irrigated canola (0.12%) (Lu et al 2018).…”

“…In Canada, crop yield data are primarily available from Statistics Canada or crop insurance programs. Recent literature regarding observed crop yield trends is limited, as many researchers have focused on projecting future crop yield changes and impacts using crop growth models when assessing the influence of climate change on crop yields (e.g., McGinn et al 1999;Smith et al 2013;Jing et al 2014;Carew et al 2017;Masud et al 2018;Qian et al 2019;Khalili et al 2021).…”

Nonparametric Estimation and Inference of Production Risk

“…Examples include Hurd (1994),; Traxler et al (1995); Smale et al (1998); Tveteros (1999); Di Falco, Chavas, and Smale (2007); Shi, Chavas, and Lauer (2013); and Sanglestsawai et al (2017), just to name a few. In addition, with increasing interest in the adverse impacts of climate change and global warming on agriculture, there has been a recent surge in the number of studies that examine how climatic variables influence production risk associated with growing major agricultural commodities (e.g., corn, wheat, rice) around the world (e.g., Chen, McCarl, and Schimmelpfenning 2004; Chen and Chang 2005; Isik and Devadoss 2006; McCarl, Villavicencio, and Wu 2008; Tack, Harri, and Coble 2012; Sarker, Alam, and Gow 2014; Arumugam et al 2015; Arshad et al 2017; Carew et al 2018; Lusk, Tack, and Hendricks 2019; Mahmood et al 2019; Guntukula and Goyari 2020).…”