Agriculture in Africa
Agriculture plays a vital role toward growth of economies of economic blocks, states and countries. It has been theorized that advances in the agricultural sector can promote shifts in labor to higher productivity sectors that offer higher income. However, agricultural production threshold in Africa and the developing world has not been met (Angela Lusigi, 1998).
Empirical research has recently outlined the significance of agriculture in the long term reduction of poverty indices in Africa. However, for agricultural production to achieve this, measures have to be put in place for driving structural change in the way agriculture is carried out. Introduction and use of chemical fertilizers is one of these measures.
Supplying nutritious, safe and affordable food to a growing population is one of the issues facing Africa at the moment. Achieving food security for her populace is by far a desirable thing. Many factors are negatively affecting agricultural production potential in Africa some of which are soil degradation, salinization of irrigated areas and climate change (BISHWAJIT GHOSE, 2014).
Yields of hybrid maize (Zea mays) could be low when grown below optimum management practices and conditions. Use of improved varieties and optimum nitrogen fertilizer will unlock the high yielding potential of hybrid maize.
This study was carried out to investigate the effect of applying different types of farm fertilizer on two varieties of maize H 624 and H 614. These two breeds are maize hybrids grown in Kenya, Africa. More specifically the question is whether or not manipulating the variety of fertilizer applied to maize breeds affects the farm yields of maize.
This paper presents results and discussions of an experiment that was conducted to test two varieties of maize in Kenya. Each variety will be tested with two types of fertilizers. Each combination will be applied to two plots of land. The yield will be measured, in 90 kg bags for each plot as shown below:
|
Yields |
||
Variety |
Farm 1 Type A fertilizer |
Farm 2 Type B fertilizer |
Farm 3 Type C fertilizer |
H 624 |
a |
b |
c |
H 614 |
d |
e |
f |
Where a, b, c, d, e and f represent yields per farm plot.Research question
The following research question guided our study:
Is there significant effect of chemical fertilizer on farm produce?
This research question was answered by sampling 3 farms and growing two types of hybrid maize on each of them. A type of fertilizer was applied on each farm.
Research hypotheses
Based on the research question, the following hypothesis can be formulated;
H0: There is no significant effect of chemical fertilizer on maize yield.
H1: there is significant effect of chemical fertilizer on farm yield.
Methods
The analysis contained in this report consists of experimental data that was obtained from secondary sources. The experiment was carried out by application of different types of fertilizers on 3 farms, type A, type B and type 3 on plot 1, 2 and 3 respectively. Each farm was subdivided into two plots. H 624 was planted in one plot and H 614 on the plot of each farm.
The method of data collection was through an experiment. A controlled study was carried out in which we attempted to understand cause and effect relationship between chemical fertilizer application and farm yields of maize.
Factors Affecting Agricultural Production in Africa
Application of type A, type B and type C fertilizers were the treatments (independent variables) while maize crop yields per acre formed the group scores (dependent variable) as a result of the applied treatments. The fertilizer types were selected at random. A randomized complete block (RCB) design was therefore designed for purposes of analysis.
For comparability purposes, fertilizer inputs were standardized. Equal rates of type A, type B and type C fertilizers were applied on the farm plots. Experimental plots were variously irrigated at equal rates at the jointing stage ensuring that all factors of crop production were met, fertilizer application being the only varying factor.
The treatments were examined on whether they had a causal effect on the dependent variable.
Data collection and analysis
The farm plots were examined for purposes of this research. Response of crop to different fertilizer types was recorded by examining leaf size and stem girth. This procedure was done periodically till the crop attained maximum growth. At harvesting, the outer two rows of the crops were not harvested for purposes of eliminating edge effects.
Each plot harvest was recorded for purposes of comparing the different yields per plot. Extra moisture was removed from the maize grains by drying in the sun before measuring the weights. The dried grains were packaged into 90kg bags and the number of bags obtained from each farm plot sub section recorded.
This analysis was conducted using the IBM Statistical Package for Social Sciences (INM SPSS) version 22.
Data source
Data source name |
Source organization |
Data description |
Data format |
URL |
Charge fee |
Target data source |
Data 1 |
Kenya government ministry of agriculture |
Maze yields |
.txt |
0.00 |
Yes |
|
Data 2 |
Food and agriculture organisation |
Maize harvest |
.txt |
1. 2. |
0.00 |
yes |
|
Yields |
||||
Variety |
Farm 1 Type A fertilizer |
Farm 2 Type B fertilizer |
Farm 3 Type C fertilizer |
Farm 4 Type A fertilizer |
Farm 5 Type B Fertilizer |
H 624 |
20 |
23 |
19 |
19 |
24 |
H 614 |
18 |
20 |
17 |
17 |
19 |
Only three of the farms were sampled for purposes of the study as shown below.
|
Yields |
||
Variety |
Farm 1 Type A fertilizer |
Farm 2 Type B fertilizer |
Farm 3 Type C fertilizer |
H 624 |
20 |
23 |
19 |
H 614 |
18 |
20 |
17 |
Results and discussion
The average maize yields for the four plot sub sections varied between 18 to 22-90 kg bags.
Normality check
Tests for normality were performed on the independent and dependent variables and Q-Q plots plotted.
Tests of Normalitya |
||||
Treatment |
Kolmogorov-Smirnovb |
|||
Statistic |
df |
Sig. |
||
Farm output |
Type A |
.260 |
2 |
. |
a. Treatment = Type A |
||||
b. Lilliefors Significance Correction |
Tests of Normalitya |
||||
Treatment |
Kolmogorov-Smirnovb |
|||
Statistic |
df |
Sig. |
||
Farm output |
Type B |
.260 |
2 |
. |
a. Treatment = Type B |
||||
b. Lilliefors Significance Correction |
Tests of Normalitya |
||||
Treatment |
Kolmogorov-Smirnovb |
|||
Statistic |
df |
Sig. |
||
Farm output |
Type C |
.260 |
2 |
. |
a. Treatment = Type C |
||||
b. Lilliefors Significance Correction |
above plots suggest that the normality assumption is met.
Univariate analysis
Treatment: Type A
Descriptivesa |
||||
Treatment |
Statistic |
Std. Error |
||
Farm output |
Type A |
Mean |
19.00 |
1.000 |
95% Confidence Interval for Mean |
Lower Bound |
6.29 |
||
Upper Bound |
31.71 |
|||
5% Trimmed Mean |
. |
|||
Median |
19.00 |
|||
Variance |
2.000 |
|||
Std. Deviation |
1.414 |
|||
Minimum |
18 |
|||
Maximum |
20 |
|||
Range |
2 |
|||
Interquartile Range |
. |
|||
Skewness |
. |
. |
||
Kurtosis |
. |
. |
||
a. Treatment = Type A |
Descriptivesa |
||||
Treatment |
Statistic |
Std. Error |
||
Farm output |
Type B |
Mean |
21.50 |
1.500 |
95% Confidence Interval for Mean |
Lower Bound |
2.44 |
||
Upper Bound |
40.56 |
|||
5% Trimmed Mean |
. |
|||
Median |
21.50 |
|||
Variance |
4.500 |
|||
Std. Deviation |
2.121 |
|||
Minimum |
20 |
|||
Maximum |
23 |
|||
Range |
3 |
|||
Interquartile Range |
. |
|||
Skewness |
. |
. |
||
Kurtosis |
. |
. |
||
a. Treatment = Type B |
Treatment: Type C
Descriptivesa |
||||
Treatment |
Statistic |
Std. Error |
||
Farm output |
Type C |
Mean |
18.00 |
1.000 |
95% Confidence Interval for Mean |
Lower Bound |
5.29 |
||
Upper Bound |
30.71 |
|||
5% Trimmed Mean |
. |
|||
Median |
18.00 |
|||
Variance |
2.000 |
|||
Std. Deviation |
1.414 |
|||
Minimum |
17 |
|||
Maximum |
19 |
|||
Range |
2 |
|||
Interquartile Range |
. |
|||
Skewness |
. |
. |
||
Kurtosis |
. |
. |
||
a. Treatment = Type C |
Tests of Between-Subjects Effects
|
|||||||||||||||
Dependent Variable: Farm output |
|||||||||||||||
Source |
Type III Sum of Squares |
df |
Mean Square |
F |
Sig. |
||||||||||
Corrected Model |
21.167a |
3 |
7.056 |
42.333 |
.023 |
||||||||||
Intercept |
2281.500 |
1 |
2281.500 |
13689.000 |
.000 |
||||||||||
Block |
8.167 |
1 |
8.167 |
49.000 |
.020 |
||||||||||
Treatment |
13.000 |
2 |
6.500 |
39.000 |
.025 |
||||||||||
Error |
.333 |
2 |
.167 |
||||||||||||
Total |
2303.000 |
6 |
|||||||||||||
Corrected Total |
21.500 |
5 |
|||||||||||||
a. R Squared = .984 (Adjusted R Squared = .961) |
Using the above results, we can fit a regression model for forecasting maize crop yields given a type of fertilizer
Multiple Comparisons |
||||||
Dependent Variable: Farm output |
||||||
Tukey HSD |
||||||
(I) Treatment |
(J) Treatment |
Mean Difference (I-J) |
Std. Error |
Sig. |
95% Confidence Interval |
|
Lower Bound |
Upper Bound |
|||||
Type A |
Type B |
-2.50* |
.408 |
.046 |
-4.90 |
-.10 |
Type C |
1.00 |
.408 |
.233 |
-1.40 |
3.40 |
|
Type B |
Type A |
2.50* |
.408 |
.046 |
.10 |
4.90 |
Type C |
3.50* |
.408 |
.024 |
1.10 |
5.90 |
|
Type C |
Type A |
-1.00 |
.408 |
.233 |
-3.40 |
1.40 |
Type B |
-3.50* |
.408 |
.024 |
-5.90 |
-1.10 |
|
Based on observed means. The error term is Mean Square(Error) = .167. |
||||||
*. The mean difference is significant at the .05 level. |
Homogeneous Subsets
Farm output |
|||
Tukey HSDa,b |
|||
Treatment |
N |
Subset |
|
1 |
2 |
||
Type C |
2 |
18.00 |
|
Type A |
2 |
19.00 |
|
Type B |
2 |
21.50 |
|
Sig. |
.233 |
1.000 |
|
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = .167. |
|||
a. Uses Harmonic Mean Sample Size = 2.000. |
|||
b. Alpha = .05. |
ANOVA table
The ANOVA table for our analysis shall be as shown below:
Tests of Between-Subjects Effects |
|||||
Dependent Variable: Farm output |
|||||
Source |
Type III Sum of Squares |
df |
Mean Square |
F |
Sig. |
Block |
8.167 |
1 |
8.167 |
49.000 |
.020 |
Treatment |
13.000 |
2 |
6.500 |
39.000 |
.025 |
Error |
.333 |
2 |
.167 |
||
Total |
2303.000 |
6 |
|||
Corrected Total |
21.500 |
5 |
|||
a. R Squared = .984 (Adjusted R Squared = .961) |
From the output F is 39.000. The treatment degrees of freedom is 2 while the error degrees of freedom is 2.
P value is 0.025.
Conclusion: Since p-value= 0.025 is less than 0.05, we shall reject the null hypothesis that
There is no significant effect of chemical fertilizer on crop yields in favor of the alternative hypothesis that there is significant effect of chemical fertilizer on crop yields.
At the 5% level of significance there is enough evidence to suggest that there is significant effect of chemical fertilizer on crop yields.
Since we have rejected the null hypothesis (there was difference in the treatment means), we perform a Turkey-Kramer (Turkey’s W) multiple comparison analysis to determine which treatment means are similar and which are different.
Multiple Comparisons |
||||||
Dependent Variable: Farm output |
||||||
Tukey HSD |
||||||
(I) Treatment |
(J) Treatment |
Mean Difference (I-J) |
Std. Error |
Sig. |
95% Confidence Interval |
|
Lower Bound |
Upper Bound |
|||||
Type A |
Type B |
-2.50* |
.408 |
.046 |
-4.90 |
-.10 |
Type C |
1.00 |
.408 |
.233 |
-1.40 |
3.40 |
|
Type B |
Type A |
2.50* |
.408 |
.046 |
.10 |
4.90 |
Type C |
3.50* |
.408 |
.024 |
1.10 |
5.90 |
|
Type C |
Type A |
-1.00 |
.408 |
.233 |
-3.40 |
1.40 |
Type B |
-3.50* |
.408 |
.024 |
-5.90 |
-1.10 |
Since none of the confidence intervals contains a zero, we can conclude that we are 95% confident that effect of all the three fertilizer types on yields differ.
Conclusion
From the findings of the research and analysis, conducted it has been shown that indeed chemical fertilizers have a significant effect on crop yields. Different types of fertilizers lead to different volumes of crop yield.
However, different types of fertilizers could respond differently based on location of the farms. Areas with insufficient rainfall or irrigation water could respond poorly to a type of fertilizer that responds well to crops grown in areas with sufficient rainfall or irrigation water.
Recommendations
Based on the study findings and conclusion, farmers should choose the right type of fertilizer to use on their farms for maximum crop production.
References
Angela Lusigi, J. P. C. T., 1998. Convergence of per capita incomes and agricultural productivity in Africa. p. 11.
BISHWAJIT GHOSE, B. R. G. S., 2014. REVIEWING THE STATUS OF AGRICULTURAL PRODUCTION IN BANGLADESH FROM A FOOD SECURITY PERSPECTIVE. Russian Journal of Agricultural and Socio-Economic Sciences, p. 9.