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ASSESSING THE IMPACTS OF CLIMATE VARIABILITY ON CROP YIELD:. The Example of Sudano-Sahelian ecological zones in Nigeria. AUTHOR. James Oladipo Adejuwon AF 23 Department of Geography, Obafemi Awolowo University, Ile Ife Nigera. OBJECTIVES.

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assessing the impacts of climate variability on crop yield

ASSESSING THE IMPACTS OF CLIMATE VARIABILITY ON CROP YIELD:

The Example of Sudano-Sahelian ecological zones in Nigeria

author
AUTHOR
  • James Oladipo Adejuwon
  • AF 23
  • Department of Geography,
  • Obafemi Awolowo University, Ile Ife
  • Nigera
objectives
OBJECTIVES
  • In general, the objective of this presentation is to assess the impact of climate variability on crop production in the arid zone of Nigeria.
  • In more specific terms, the paper is designed to analyze inter-annual changes in crop yield as responses to inter-annual climate variability in the Sudano-Sahelian zones in Nigeria, using Bornu and Yobe states as a case study.
slide5
In the current exercise, our first recourse was to define impact in terms of the strength of the sensitivity of crop yield to inter annual changes in climate.
  • However the correlations between crop yield and the total annual or growing season rainfall proved to be so weak that they were inadequate to capture the essence of the impacts of climate variability on crop yield.
slide6
The disaggregates of the growing season rainfall, specifically, June, July, August and September rainfall, show significant correlations with crop yield.
  • Higher or more significant correlations were achieved when these disaggregates were related to crop yield in multivariate linear models.
  • The main weaknesses of the linear model approach were that it was not able to differentiate between negative and positive impacts and at the same time recognize the much broader band of years with normal or near normal levels of yield.
slide7
To solve this problem, the crop yield array was converted to a z- distribution format varying in magnitude from – 3 to + 3.
  • Whenever the anomaly is significant, it is counted as an impact
  • From this format, we could discriminate significant positive and significant negative impacts, separated by the normal yields levels.
  • We recognized that the yields of most of the years belong to this near normal category of impacts.
slide8
It is clear that the impact of climate on crop yield varies from year to year as hypothesized.
  • For a great majority of the years covered by the study, what could be described as normal yield levels were the order.
  • However, in certain years, negative yield anomalies signified negative impacts of climate just as in other years positive anomalies signified positive impacts.
slide9
The years during which impacts of crop yield were observed varied from one crop to the other.
  • For example, it was only in 1990 that negative anomalies were recorded for all the crops.
  • However, the magnitude of the anomalies amounted to impacts with respect to only three of the crops.
  • In 1988, the negative cow pea yield anomaly also amounted to an impact.
slide10
In general climate impacts were least on the yield of rice among the six crops considered.
  • This was evident from the values of the coefficients of correlation.
  • It was also evident from the low magnitudes of the yield anomalies over the 17-year period.
  • This could be due to the fact that rice is more likely to be irrigated than the other crops.
slide11
The crop whose yield is most likely to be impacted by climate variability turned out to be cow peas.
  • The next is maize followed by millet.
  • Negative impacts of climate are more likely to result from inadequate rainfall in June, September or both months.
slide13
The yield recorded for maize in 1990 represented a significant negative anomaly while that for 1993 represented a significant positive anomaly.
slide15
For sorghum, the only significant anomaly was negative and was recorded for 1990.
  • Also only one significant negative anomaly was recorded for cow peas and that was for 1988.
  • G/nuts yields also rose significantly higher than the normal in 1997.
  • It thus appears that the worst year for crop yield was 1990 during which significantly low levels of crop yields were recorded for the three major cereal crops: maize sorghum and millet.
slide20
Two significant anomalies were recorded for millet: one negative, 1990, and the other positive, 1994.
slide22
If inter annual variation in rainfall is the main cause of anomalies in crop production, this should be revealed in the rainfall patterns of 1990.
  • In 1990, with the exception of cow peas, all the crops recorded their highest negative yield anomalies for the period from 1983 to 1999.
  • Cow peas also recorded yield anomaly that was the second highest for the period.
slide23
1990 indeed recorded the lowest growing season rainfall from 1983 to 1999.
  • This amounted to 319 mm for the four months, June, July, August, and September.
  • The 25 mm rainfall of June was grossly inadequate to wet the fields in preparation for tilling the soil and planting the crops.
slide24
Rain stopped abruptly in September.
  • There was no rain during the first two weeks of the month when cereal crops planted in the middle of July would be at the period of grain filling.
  • The 19.3 mm of rain fell in two showers of 12 mm and 7 mm respectively.
slide25
Positive climate impacts were recorded in 1993, 1995 and 1997, respectively for maize, millet and ground nut.
  • In the other years crop yield anomalies did not attain the levels at which they could be considered as impacts.
slide26
At the present, climate forecasts in West Africa are for the total seasonal rainfall.
  • This is definitely not sufficient for forecasting crop yields or the impacts of climate.
  • There is thus an urgent need for forecasts of onset and termination of the growing season.
slide27
Following these findings, our main conclusion is that what the farmer needs from the weather forecasting community is the forecasts pertaining to June and September.
  • It is recognized that, for now, this may be a tall order given the low skill levels of the existing capacity for extended range weather forecasts (Adejuwon and Odekunle, in press).