Response of Maize to Planting Dates in a Tropical Rainforest Location

Fakorede, M. A. B.

doi:10.1017/s0014479700012217

Cited by 23 publications

(26 citation statements)

References 7 publications

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“…Baseline yields for March plantings were higher than those for May plantings for all the crops. This confirms the findings of Fakorede (1985) that grain yield of maize decreases by 30, 38, and 34 kg ha -1 for each day that planting is delayed after the first planting in March 1978March , 1980March and 1981 respectively. The effects on yields of increases in rainfall of up to 15% are marginal, regardless of whether crops were planted in March (at the beginning of the season) or May (when the rainy season was well under way) ( Table 2).…”

Section: Interpretation Of Epic-run Outputssupporting

confidence: 90%

“…The other climatic-determining factors play a constraining role in reducing the potential yields (Williams et al 1989). This conforms to the explanations of Fakorede (1985) and Osafo (1976) regarding the higher yields of March compared with May plantings of maize. It also conforms to our observations from an earlier study, to the effect that lower amounts of solar radiation that characterize the main growing season months in Nigeria tend to depress the yield of maize (Adejuwon 2002).…”

Section: Effects Of Changing Climate Parameterssupporting

confidence: 87%

“…The same strategy was adopted to assess the possible effects of changing RH on crop yield, also using an initial data file created for Ilorin. Fakorede (1985) tested the effects of increasing rainfall and increasing RH on the yields of crops planted early (March) and later (May), observing that planting early in the rainy season tends to result in higher yields.…”

Section: Response Of Crops To Individual Climate Parametersmentioning

confidence: 99%

“…The decrease in yield associated with delayed planting dates has been explained in an experiment conducted on the research farm of Obafemi Awolowo University, Nigeria (Fakorede 1985). The decrease in yield was ascribed to the higher levels of solar radiation received during the earlier parts of the growing season.…”

Section: Effects Of Changing Climate Parametersmentioning

confidence: 99%

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Food crop production in Nigeria. II. Potential effects of climate change

Adejuwon¹

2006

Clim. Res.

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Two separate lines of investigation were adapted from the recommendations of the 1994 United States Country Studies Program (USCP). In one approach, arbitrary incremental scenarios were adopted to assess the response of crop yield to changes in the various elements of climate. Using the erosion productivity impact calculator (EPIC) crop model, tests were conducted to demonstrate how crop yield might respond to changes in rainfall, relative humidity (RH), temperature, solar radiation and CO 2 concentration. The value of one element was altered incrementally while holding constant the values of the others. In the second approach, potential future changes in the yields of maize, sorghum, millet, rice and cassava were simulated based on a scenario specifying mean conditions for 1961-9190 as a baseline and general circulation model (GCM) projections from this for 3 periods: 2010-2039, 2040-2069 and 2070-2099. The results predicted a possible increase in crop yield during the first half of the 21st century to be followed by a decrease during the second half. Enhanced crop yield was explained by projected increases in rainfall, solar radiation, atmospheric humidity and CO 2 concentrations. Lower yields were explained in terms of continued global warming, as a result of which maximum and minimum temperatures approach the limits of tolerance for the modelled crops. KEY WORDS: Climate change · Crop yield · Nigeria · Food productionResale or republication not permitted without written consent of the publisher

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Section: Interpretation Of Epic-run Outputssupporting

confidence: 90%

Section: Effects Of Changing Climate Parameterssupporting

confidence: 87%

Section: Response Of Crops To Individual Climate Parametersmentioning

confidence: 99%

Section: Effects Of Changing Climate Parametersmentioning

confidence: 99%

See 2 more Smart Citations

Food crop production in Nigeria. II. Potential effects of climate change

Adejuwon¹

2006

Clim. Res.

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“…Acikgoz [1] in his studies on effect of sowing time and planting method on rice yield per day also used factorial experiment. Fakorede [12], in maize planting date trial, used a randomised complete block design (RCBD) with a split-plot arrangement. He assigned dates to main plot, plant density to sub plot and genotypes to …”

Section: Introductionmentioning

confidence: 99%

Using Residual Analysis to Validate Rice Sowing Dates Experiment Model

U.¹,

Vange²,

Chigbu³

2009

Appl Ecol Env Res

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Abstract. Picking a model for a problem is a major undertaking. If the model fits well then it can be used to increase understanding of the problem and/or for prediction. Several statistical procedures have been used by researchers for planting date studies to suit their objectives. The ever-proliferation of Statistical procedures available to researchers has given room for use of diverse statistical design for research into finding optimum planting time for crops. Considering the subtle differences, advantages and disadvantage that these statistical designs pose, the results of such analysis may lead to false conclusion or be less reliable at least for comparative purposes. There is need to look at date of planting trials again to see the possibility of proffering a statistical model that could be commonly used by researchers. The main purpose of this work is to apply the residual analysis to check the suitability of the series of similar experimental model to describe the effects of sowing dates on yield of upland rice with the view of predicting optimum sowing date. Results show that the series of similar experiment methodology is able to model the changes associated with different sowing dates. The questions associated with model adequacy were discussed. Keywords: Sowing dates, rice, statistical model, residual analysis IntroductionStudies on dates of planting have been extensively conducted for several crops across different ecological zones. A review of studies on rice planting dates indicates that these research were necessitated by weather pattern [6,38]; pest management [5,18,32,36,37]; disease management [2,20]; and grain quality [9].Several statistical procedures have been used by researchers for planting date studies to suit their objectives. Obi [23] proposes series of similar experiment models for time of planting experiments. Some researchers [3,25,27], used this series of similar experiment model in their study. A survey of other works on planting date show that [35] used a randomized block design with four complete blocks to study the effects of planting dates and residue rate effect on growth partitioning and yield of corn. Also [2,18] used randomised complete block design in their planting date trials, while [28] in their trial on response of Soybean lines with "juvenile" trait to day length and sowing date combined both greenhouse and field work. In both cases they used factorial arrangement. Acikgoz [1] in his studies on effect of sowing time and planting method on rice yield per day also used factorial experiment. Fakorede [12], in maize planting date trial, used a randomised complete block design (RCBD) with a split-plot arrangement. He assigned dates to main plot, plant density to sub plot and genotypes to

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Seed Technology for Sustainable Maize Production in Sub-Saharan Africa

Badu‐Apraku

Fakorede

2017

Advances in Genetic Enhancement of Early and Extra-Early Maize for Sub-Saharan Africa

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Response of Maize to Planting Dates in a Tropical Rainforest Location

Cited by 23 publications

References 7 publications

Food crop production in Nigeria. II. Potential effects of climate change

Food crop production in Nigeria. II. Potential effects of climate change

Using Residual Analysis to Validate Rice Sowing Dates Experiment Model

Seed Technology for Sustainable Maize Production in Sub-Saharan Africa

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