Climate change, weather variability and corn yield at a higher latitude locale: Southwestern Quebec

Almaraz, Juan J.; Mabood, Fazli; Zhou, Xiaomin; Gregorich, E.G.; Smith, Donald L.

doi:10.1007/s10584-008-9408-y

Cited by 96 publications

(65 citation statements)

References 14 publications

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“…This is consistent with the results of Lauer et al (1999) who reported that grain corn yields decreased with later planting date. In our study, the increase in yield between the two groups of site-years could be explained by the planting date (Mullen et al, 2010), but other parameters such as soil types, hybrids and weather conditions may be involved (Almaraz et al, 2008;Ziadi et al, 2013;Tremblay et al, 2012;Woli et al, 2016). The EONR for the site-years planted before mid-May ranged from 180 to 237 kg N ha -1 (Table 4).…”

Section: Site Id Yearmentioning

confidence: 60%

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Variability in Corn Yield Response to Nitrogen Fertilizer in Eastern Canada

Kablan¹,

Chabot²,

Mailloux³

et al. 2017

Agronomy Journal

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Corn (Zea mays L.) yield response to N has been found to vary spatially within a field. The objective of this study was to examine how grain corn yield response to N varies with planting date, soil texture, and spring weather across sites and years in the Montérégie region. Trials were conducted from 2002 to 2004 and 2006 to 2010, at 11 sites with 23 hybrids and four N application rates, for a total of 45 site-years. Each site-year involved five or six N rates ranging from 80-90 to 240 kg N ha -1 . Grain yield response to N rates varied among site-years. Trials were separated into two groups based on optimal and late planting dates. Significant differences in grain yield among the applied N rates were observed in all of the site-years planted at optimal dates (from 8.8-14.7 Mg ha -1 ), and in most of those planted late (8.5-12.8 Mg ha -1 ). Economic optimum nitrogen rates (EONR) ranged less widely for site-years planted on optimal dates (180-237 kg N ha -1 ) than for those planted late (132-237 kg N ha -1 ). The EONR was affected by soil textural classes and rainfall. On coarse-textured soils, more N was needed to optimize grain yield in years with wet growing seasons. These results suggest that the current N recommendations for corn in Quebec should consider the variability in response associated with site-specific effects of planting date, soil texture, and weather.

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Section: Site Id Yearmentioning

confidence: 60%

“…In the Montérégie region, Almaraz et al (2008) demonstrated that May precipitation and July temperature were strongly associated with yield variability in corn fields, and these climatic factors explained more than half of this variability.…”

Section: Economical Optimal Nitrogen Rate Asmentioning

confidence: 98%

Variability in Corn Yield Response to Nitrogen Fertilizer in Eastern Canada

Kablan¹,

Chabot²,

Mailloux³

et al. 2017

Agronomy Journal

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“…Moreover, climate change triggers increased temperatures, changing rainfall, patterns and amounts, and a higher frequency and intensity of extreme climate events such as floods, cyclone, droughts and heat wave [17] [18]. However, temperature increases and erratic rainfall patterns affect crop agriculture mostly directly cum adversely [19] [20] [21], and by extension, food I. N. Nwachukwu, C. A. Shisanya 4/10 OALib Journal security. Changes in climate generally involve changes in two major climate variables: temperature and precipitation.…”

Section: Methodsmentioning

confidence: 99%

Determinants of Agricultural Production in Kenya under Climate Change

Nwachukwu¹,

Shisanya²

2017

OALib

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Climate change has been described as the most significant environmental threat of the 21 st century with vast impact mostly on agriculture, altering food production processes. As an important sector in the Kenyan economy, agriculture continues to dominate other sectors despite its declining contribution to real GDP. Given that the performance of the agricultural sector is determined by a large number of factors, the need to articulate this study has become imperative in view of the climate phenomenon. Therefore, the study examined the determinants of agricultural production in Kenya under climate change with specific interests in assessing the trend of climate variables and growth rate of agricultural production within 1970-2012 periods; estimating factors influencing agricultural production and deducing policy implications from the findings. Data used were secondary and include value of agricultural production, livestock, machines, fertilizer, agricultural land, labour, annual precipitation and temperature over the study period. These were obtained from databases hosted by the Food and Agriculture Organization (FAOSTAT), the World Bank and United Nations Development Programme (UNDP). Data analysis was done using trend analysis, log quadratic trend equation and multiple regression model. The trend results show that precipitation pattern traced out high amplitude decadal variability with the forecast showing off a slightly upward trend. In temperature, the inter-annual variability observed was wide with a forecast of a slight increase up to 2020. In terms of growth analysis, the log quadratic equation indicates that agricultural production posted a compound growth rate of 3.252% during the period while production was determined by the quantum of labour, livestock, agricultural land, precipitation and its squared term. The implication is that precipitation both on the short and long run affected agricultural production. It is recommended that ecosystem based and technologically driven adaptation measures be taken to address climate change effects on agricultural production.

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“…Crop simulation tools are effective in this exercise, but the regression based approaches for understanding the dependence of growth and yield of crops with the historic weather data are also important. Regression models that use historical data on both climatic variables and yields are capable of providing accurate estimates of the changes in crop yield as a result of changes in climatic variables (Almaraz et al, 2008). However, such climatic variability is not reported with respect to crop yield variations.…”

Section: Introductionmentioning

confidence: 99%

Climatic Variability and Wet Season Rice (<i>Oryza sativa</i> L.) Production in North-West Bangladesh

et al. 2017

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Climate change is influencing rice (Oryza sativa L.) production in some agro-ecological regions of Bangladesh. The impact of seasonal climatic variability on rainfed lowland rice (transplanted aman or T. Aman) yield in north-west Bangladesh was analyzed based on historic weather data from 1971 to 2010. Wet season maximum and minimum temperatures were increasing by 0.0174 and 0.0083°C year . Combined effect of increased minimum temperature and decreased sunshine hours will govern T. Aman rice yield in future.

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Climate change, weather variability and corn yield at a higher latitude locale: Southwestern Quebec

Cited by 96 publications

References 14 publications

Variability in Corn Yield Response to Nitrogen Fertilizer in Eastern Canada

Variability in Corn Yield Response to Nitrogen Fertilizer in Eastern Canada

Determinants of Agricultural Production in Kenya under Climate Change

Climatic Variability and Wet Season Rice (<i>Oryza sativa</i> L.) Production in North-West Bangladesh

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