Article Type: Research Article Article Citation: Reem Fadul Kabbar, Esmail Alfadul, and Abdelmoneim
A. Babiker. (2020). CONTRIBUTION OF AGRO-ECOSYSTEM IN IMPROVING AGRICULTURAL
RESILIENCE AMONG FARMERS IN WHITE NILE SUDAN. International Journal of Research
-GRANTHAALAYAH, 8(8), 173-180. https://doi.org/10.29121/granthaalayah.v8.i8.2020.860 Received Date: 24 July 2020 Accepted Date: 27 August 2020 Keywords: Agro-Ecosystem Agricultural Resilience Sudan This paper was designed to assess contribution of agro-ecosystem after intervention in improving agricultural resilience among farmers in White Nile State of Sudan. The study was based on primary data collected from 200 farmers by a questionnaire. A stratified sampling method was followed to select 200 farmers. Descriptive statistics (frequency and percentage Model of agricultural resilience consisted of two important dependent variables income from agriculture and adaptive capacity of the farmers against the changes in agro-ecosystem. farmers had many practices to adapt with change in agro-ecosystem. The results of analysing the model of agricultural resilience discovered males as more resilient than females. The factors age, years of experience and years of education were found to increase agricultural resilience of the farmers. Moreover, farmers of sesame and peanut were more adaptive to the change in agro-ecosystem than farmers of the sorghum. The study also revealed that farmers benefited significantly from Khor Abu Habil Water. Regarding to the local knowledge of the farmers in the study area there were natural predators helping in defeating insects, the results discovered that negative relationship between the conflicts in the study area and the income from agriculture. The more conflict existing in the area the less income from agriculture will be achieved.
1. INTRODUCTIONAgriculture is a multi-faceted concept that contains a wide range of productive systems. Potential benefits and disadvantages of agriculture to ecosystem services will be mainly shaped by the typology of the agro-ecosystem, (FAO, 2011). Resilience is the capacity of a system, deal with change and continue to develop. It is capacity to use in shocks and crisis or climate change to spur renewal and innovative thinking. Resilience thinking embraces learning, diversity and above all the belief that humans and nature are strongly coupled to the point that they should be conceived of as one social-ecological system (Moberg and Simonsen, 2013). Resilience is not just about the ability to maintain or return to a previous state; it is about adapting and learning to live with changes and uncertainty. There are three types of capacity that are important in helping people do this: (i) absorptive capacity, that is, the ability to cope with, and absorb the effects of shocks and stresses (ii) adaptive capacity, that is, the ability of individuals or societies to adjust and adapt to shocks and stresses, but keep the overall system functioning in broadly the same way – for instance when a household decides to diversify its crops in order to respond to changing weather conditions; (iii) transformative capacity, that is, the ability to change the system fundamentally (Béné , 2013).Agricultural resilience is about providing farmers to absorb and recover from shocks and stresses to their agricultural production and livelihoods. (Grady, 2011). Increasing resilience can be achieved by reducing vulnerabilities and increasing adaptive capacity. This can be achieved by reducing exposure, reducing sensitivity and increasing adaptive capacity, for every type of risk. In this paper focus was on how the intervention of Tandalti dam - built in 2007 at the pathway of Khor Abu Habilcan - support agricultural activities by many changes in Agro-Ecosystem happened which had an impact on agricultural production. Therefore, this paper is seeking to assess Agro-Ecosystem in improving agricultural resilience among farmers in the Semi-arid Khor Abu Habil, Sudan. 2 RESEARCH METHODThe primary data used in this paper were collected from the respondents by using a questionnaire. The study included all farmers in Tandalti locality on the pathway of Khor Abu-Habil 60 km north of the Tandalti Dam and 40 km south Tandalti Dam. Farmers were 2000 distributed to 32 villages. A stratified sample was used to select the sample size. Descriptive statistics (frequency and
percentage) has been used to describe the characteristics of the respondents.
STATA Software was used to implement the Ordered Logistic Regression Model. The
purpose of the model was to assess the contribution of agro-ecosystem in
improving agricultural resilience in semi-arid Khor Abu Habil. Various
Inflation Factors (VIF) was determined to know how much the variance (the
square of the estimate's standard deviation) of an estimated regression
coefficient is increased because of co-linearity. (VIF) was 1.74 and that less
than 2, which was acceptable. R-squared was 0.8017 that means the15 independent
variables were going to explain the two-dependent variable (income from
agriculture and adaptive capacity of farmers, which represent the agricultural
resilience in the model) by 80% and with standard error 20%. The Probability of
the model was less than 0.05 and it was 0.001, which was highly significant
that means the model was proper. Table 1: Assumptions Model
3.
RESULTS
AND DISCUSSIONS
Table 3.1: Agricultural Resilience Model
3.1. AGRICULTURAL RESILIENCE MODEL CON
Source: Field Survey (2018) 3.2. DISCUSSION OF SOCIOECONOMIC VARIABLES
3.2.1. FACTOR OF AGE Other
socioeconomic factors such as age, which contributed positively on agricultural
income, but it was not significant (0.438) at 0.05 significance level and
coefficient (0.11915) as table 5.1 showed. That means farmer will gain more
money from agriculture when getting older. The sign of coefficient of the age
was against to the hypothetical sign of age in the table of the expectation
sign. In addition, age with adaptive capacity was not significant (0.61) and
coefficient (-.00315) as table 3.1 showed. The sign of the coefficient
(-.00315) was negative which means the young farmers were more adaptive to the
changes in agro-ecosystem rather than old farmers. As a result, the young
farmers were more resilient than old farmers. 3.2.2. FACTOR OF GENDER Many factors
affected the agricultural resilience in semi-arid Khor Abu Habil positively and
negatively. Gender was one of these factors. The income from agriculture will
be increased to the direction of the male rather than female. The reason behind
this was mentioned previously in table 3.1 that the men had more effort in
agriculture than female in study area. In addition, men were responsible for
the marketing process. Besides that, women in study area had more
responsibility beside their work in agriculture such as cooking and cleaning,
taking care of children and other housing staff. On one hand, the variable of gender with
income from agriculture was significant (P. value 0.036) at 0.05 significance
level with coefficient (1.108). However, also the interpretation was going in
the direction of the male. The numeric interpretation that every man will gain
110.8 SDG from agriculture more than woman did. On the other hand, male were
more adaptive to change in agro ecosystem rather than female but it was not
significant (0.363) see table 3.1 Therefore males were more resilient than
females. 3.2.3. FACTOR OF YEARS OF EXPERIENCE Years of
experience of agricultural practices affected the agricultural resilience
significantly (0.01) at 0.05 significance level. The coefficient was (0.7458)
and it was positive. That mean the income from agriculture will be increased
when the farmer had more experience in agricultural practice. Other numeric
interpretation when the farmer had one more experience year the income from
agriculture will increase by 74.58 SDG. On the other hand, the coefficient of
years of experience and adaptive capacity was positive (4.8047) and it was not
significant (0.546) as in table 3.1Therefore, the capability of the farmers to
adapt with changes in agro-ecosystem will be increased when the farmers have
more years of experience in agriculture. Therefore, farmers will be more
resilient with more years of experience in agriculture. 3.2.4. FACTOR OF AGRICULTURAL PATTERN As table 5.1
showed, there were two main agricultural patterns in the study area. Flooding
agriculture and rain fed agriculture. Agricultural pattern with income from
agriculture was not significant (P. Value 0.94) at 0.05 significance level but
the coefficient was positive (0.03063) see table 3.1. However, the
interpretation was going to the direction of flooding agriculture, the more
flooding agriculture be practiced the greater income from agriculture be
achieved. Also the interpretation of agricultural pattern with adaptive
capacity, which was not significant ( P. value 0.894) at 0.05 significance
level with positive coefficient (0.02268), was going to the direction of the
flooding agriculture. The farmer of flooding agriculture were more adapted to
the change in agro-ecosystem. Therefore, farmers of flooding agriculture were
more resilient than rain fed farmers, who grow away of the pathway of Khor Abu
Habil. The reason behind this was that the rain fed farmers were more
vulnerable to hazards of agro ecosystem especially drought and sand creeping.
In addition, they were more adapted to herbicide and pesticide that rain fed
farmers see table 5.2. 3.2.5. FACTOR OF YEARS OF EDUCATION The variable
of years of education has an effect on the agricultural income positively, but
it was not significant (0.278) with positive coefficient (5.063). Which means
the income from agriculture will increase by increasing the years of education.
Other numeric interpretation, increasing one year of education will increase
the income from agriculture by 506.3SDG. In addition, the adaptive capacity of
the farmers will increase by increasing the years of education. The conclusion
was that the more educated famers the better resilient status. This result was
confirmed by a study in Zimbabwi which was conducted by (Ellen Chigwanda,
2016). The study had mentioned that Education has a great contribution on
building resilience of the community. Therefore, having an education can
prepare a girl to cope better with droughts and climate change. This study
specifically focused on how droughts lead to girls missing school and to
explore the interplay between girls’ education and climate change. 3.3. DISCUSSION OF AGRO ECOSYSTEM VARIABLES
3.3.1. CROPS PRODUCTIVITY Sesame in
Sudan is considered one of the most important cash crops. It had significant
contribution on the income from agriculture in study area. According to table
5.3, 38.7% of the farmers grew the sesame. The coefficient was 1.733 and p.
value was 0.000 at 0.05 significance level as in table 3.1 The numeric
interpretation of this result is when increasing sesame productivity by one kg
per fed will increase the income from agriculture by 173.3 SDG. Sorghum was
one of the most important crops to farmers in the study area. It was used to
feed themselves and their animals. It had significant contribution on the
income from agriculture in study area. According to table 5.3 that 34.7% of the
farmers grew the sorghum. The coefficient was 0.8746 and p. value was 0.000 at
0.05 significance level as in table 3.1. The numeric interpretation was when
increasing sorghum productivity by one kg per fed it will result in income
increase from agriculture by 87.4 SDG.Peanut was one of the cash crops in the
study area. According to group discussion, many farmers grew the peanut for
marketing reason. In addition, some of the crop is used to feed themselves and
their animals. It had significant contribution on the income from agriculture
in study area. According to table 5.3 that 38.7% of the farmers grew the
peanut. The coefficient was 0.1005 and p. value was 0.000 at 0.05 significance
level as in table 3.1. The numeric interpretation is that increasing peanut productivity by one kg per
fed will increase the income from agriculture by 100 SDG. Similarly, in term of
variable of adaptive capacity the signs of the coefficient of farmers of sesame
and peanut were positive (0.000033 and 0.00031 consequently). And the sign of
sorghum was negative (-0.000114) as in table 3.1Therefore the conclusion will
be that farmers of sesame and peanut were more adaptive to the change in
agro-ecosystem rather than farmers of the sorghum, but this was not
significant. The reason behind this may be the pests that threatened the crop
recently as mentioned in table 5.3. As a result agricultural productivity
(sesame and peanut) were contributed significantly in agricultural resilience
of the farmers. The more agricultural production will be gained the more
resilience will be built. 3.3.2. FACTOR OF NATURAL FERTILIZATION Recently
animals in the study area were introduced to the crops rotation system see
table 5.4. However, farmers benefited from agricultural residual as dry forage
to feed animals, which produce natural fertilization. The coefficient was 4.200
and the p. value was (0.010 at 0.05 significant level). According to this
result, using natural fertilization contributed significantly in the income
from agriculture. The more natural fertilization will be used the greater
income will be gained. Other numeric interpretation is that using one unit of
natural fertilization per fed will increase the income from agriculture by 420
SDG. Therefore, natural fertilization supported the agricultural resilience in
the study area. 3.3.3. FACTOR OF BURNING EXCREMENT OF ANIMALS In the time
being many insects and pests had appeared in the study area. There were more
than 74% of farmers observed a new pest and insects in the area since the
Tandalti Dam was established in 2007 (see table 5.5). Farmers had their own
ways to protect themselves, crops and their animals. Burning excrement of animals was a successful
way. The coefficient was 0.00125 with positive sign and the p. value was (0.010
at 0.05 significant level). That means burning excrement of animals contributed
significantly in the income from agriculture. Hence, the technique of burning
excrement of animals support the agricultural resilience in the study area. The
more pests and insects being fought by burning excrement of animals, the
greater agricultural resilience will be built. 3.3.4. FACTOR OF KHOR'S WATER Khor Abu Habil
carries water only periodically during the rainy season. It originates in the
Nuba Mountains and flows along a pre-defined pathway of channels (Seifelislam,
2017). The Khor water support the flooding agriculture, which was practiced by
more than 54% of the farmers in the study area and was considered as the main
agricultural pattern (see table 5.1). The sign coefficient was positive and the
p. value was significant (0.05 at 0.05 significant level) see table 3.1. Therefore,
Khor’s water had a contribution on the resilience of the farmers in the study
area. This result was a confirmed result to the fact that farmers were
benefited from the flooding agriculture mentioned previously hence, the
conclusion that Khor Abo Habil supported the agricultural resilience
significantly. 3.3.5. FACTOR OF NATURAL PREDATORS Regarding to
the local knowledge of the farmers in the study area there were natural
predators helping in defeating the insects, which was significant (0.02 at 0.05
significant level) with adaptive capacity. The interpretation was going in the
direction of farmers who had a strong adaptive capacity. Natural predators will
be used more by the strong adaptive farmers with the change in agro-ecosystem.
In the same way, natural predators contributed positively with income from
agriculture. However, it was not significant. Therefore, the conclusion was the
natural predators contributed positively on the agricultural resilience of the
farmers in the study area. 3.3.6. FACTOR OF CONFLICT IN THE STUDY AREA Recently there
was a conflict in the study area between farmers and agro pastoralist. More
than 59% of the farmers suffered from such conflict as table 5.6 showed. This
conflict had negative effect on the income from agriculture and at the same
time on the community stability. The results discovered that negative
relationship between the conflict in the study area and the income from
agriculture by determine the negative coefficient -10.68. Meanwhile the
relation was significant (0.016 at the 0.05 significant level). Therefore, the
more conflict existing in the area the less income from agriculture will be
achieved. 4.
CONCLUSIONS
AND RECOMMENDATIONS
The
agricultural resilience of farmers in semi-arid Khor Abu Habil was built
through enhancing the agricultural productivity and increasing the capability
of farmers to adapt with changes in agro-ecosystem. Agro-ecosystem on Khor Abu
Habil helped farmers to be resilient. Males were more resilient than females.
In addition, by increasing age, years of experience and years of education
agricultural resilience of the farmers will be increased. Sesame, Peanut, and
Sorghum contributed significantly on agricultural resilience. Farmers of
flooding agriculture were more resilient than traditional farmers. Using
natural fertilization and natural predators helped farmers to be more resilient
in the study area. Existing conflict between the pastoralists and farmers in
the study area affected negatively the resilience of the community in the study
area. APPENDICES
Table 5.1: Distribution of the
Respondents by Agricultural Pattern
Source: Field Survey (2018) Table
5.2: Methods of Agricultural Pest Control before and after the Construction
of the Tandalti Dam
Source:
Field Survey (2018) Table 5.3: Distribution of the
Respondents by the Main Crops are Grown by Farmers
Source: Field Survey (2018) Table 5.4: Distribution of the
Respondents by Benefits from the Agricultural
Residues
Source: Field Survey (2019) Table 5.5: Distribution of the
Respondents by Using of Indigenous Knowledge in
Determining Rain Time
Source: Field Survey (2018) Table 5.6: Existence of the Conflicts in the
Area
Source: Field Survey (2019) SOURCES OF FUNDINGThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. CONFLICT OF INTERESTThe author have declared that no competing interests exist. ACKNOWLEDGMENTNone. REFERENCES
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