Assessing the effects of agroecology and conventional farming techniques on small-scale peasant farmers ’ crop yields in the Fako and Meme divisions of Cameroon

Small-scale farming constitutes a very important segment of the food production chain in most third world countries. In Cameroon for example, they constitute about 70% of the agrarian population. This study aimed at verifying the effects of agroecology and conventional farming techniques on crop yields in four sites in the South West Region of Cameroon from small-scale farms. Data were obtained through the administration of 200 questionnaires and two focus group discussions (FGDs). The data were analyzed using frequencies, means, coefficient of correlation, coefficient of determination, and linear regression models. The FGDs were also analysed using context analysis. All the analyses were performed in SPSS version 20 and Wordstat 7 software. The results showed that both agroecology and conventional farming techniques are used in the study sites but agroecology techniques are more responsible for yield increases than conventional techniques as seen in correlations coefficients and regression outputs. The only exceptions in which conventional farming techniques contribute more to yields was under income levels and the number of family members that live and work on the farm. This was justified by the fact that conventional techniques often require higher income levels since they are often purchased.


INTRODUCTION
Agriculture is important to Cameroon's economy, because, it employs between 70 and 80% of the population, accounts for close to 50% of export earnings and contributes 30% to the Gross Domestic Product (GDP) of the country (DSCN, 2002).A majority of the population involved in agriculture is dominated by small-*Corresponding author.E-mail: terence.epule.epule@umontreal.ca.
Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution License 4.0 International License scale farmers and their family members who constitute about 70% of the agricultural population (National Institute of Statistics, 2010).
Cameroon is currently experiencing declines or stagnation in food production (Epule and Bryant, 2015).Much of this decline or stagnation has been attributed to declines in soil nutrients (Gould et al., 1989;Nkamleu and Adesina, 2000;Kombiok et al., 2013), population growth and resultant expansion of agricultural land through deforestation (Palm et al., 2001;Epule and Bryant, 2015).As a result of these observations, a lot of efforts are being made to increase crop yields and reduce food insecurity.Much of these efforts have been based on the use of agroecology or conventional farming inputs such as organic and inorganic fertilizers, respectively.These have different repercussions on the environment and on crop yields.The use and adoption of conventional farming inputs such as inorganic fertilizers have positive impacts on crop yields, but as well they have also had adverse effects on human health, soils, and water resources.To make things worse, small-scale farmers are often unable to secure several conventional inputs because of the high cost involved (Drechsel et al., 2001;Tittonell et al., 2005;Brock and Foeken, 2006;Schmidhuber and Tubiello, 2007;Lindell et al., 2010a, b;Epule et al., 2012).The debate assumes that organic inputs increase yields and limit environmental degradation while inorganic inputs increase yields but degrade the environment.As such, being that these assertions are not well grounded, it still remains unclear which of agroecology or conventional farming techniques do increase crop yields more.
In the western world and the global south, agricultural production has been based on a dominant agricultural model that is anchored on: (1) mechanization and monoculture, (2) commercial crops, market development and globalization and integration, and (3) inorganic fertilizers and crop hybrid seeds of all types (Matson et al., 1997;Drechsel et al., 2001;Tittonell et al., 2005;Godfray et al., 2010;Epule et al., 2012;Snapp et al., 2010).This model has been associated with problems of agricultural production stagnation and decline in the global south due mainly to problems of poverty and access to these external inputs.Therefore, the potential of crop yield stagnation increases with the poverty level as it is the case in most developing regions of the world (Rosegrant and Svendsen, 1993;Matson et al., 1997;Hossain and Singh, 2000;Adesina et al., 2000;Reid et al., 2003;Lindell et al., 2010a, b;Epule et al., 2012).Alternatively, the agroecology approach may offer a new way forward.It is based on production that uses natural nutrient cycling with little or no synthetic substances and consequently easily accessible to small-scale farmers (Bezner-Kerr et al., 2007;Badgley et al., 2007;Snapp et al., 2010).This approach could lead to crop yields improvements through four pathways: (1) accumulation of organic matter and nutrient cycling through the use of natural processes; (2) natural control of diseases and pests through the use of nets and predator-prey strategies rather than the use of chemicals; (3) conservation of resources such as water, soil, biodiversity and energy through major processes of sustainable water management through irrigation (Valipour 2014a(Valipour , b, c, d, e, f, g, 2015;;Valipour et al., 2015); and (4) improvement of biological interactions, biodiversity and synergies between agricultural sub-systems.Some of the reasons underlying this new model are based on attempts at answering questions related to ways of feeding the population in the midst of an old model that is not working adequately as reflected in a rise in the prices of inputs such as inorganic fertilizers.In addition to the cost associated with inorganic fertilizers, they also have negative environmental effects.
The present study uses population perceptions to evaluate the relative effects of agroecology and conventional farming techniques on crop yields among small-scale farmers in the Meme and Fako divisions of the South West Region of Cameroon.The analyses of the adoption of agroecology and conventional techniques, the reasons for the adoption of the latter methods, and the coping mechanisms adopted by the farmers are not within the scope of this current study and are simply part of a larger research project on agroecology and conventional farming techniques in parts of Cameroon.This study specifically aimed at verifying the relative effects of agroecology and conventional farming techniques on crop yields in four sites in Cameroon.

Study area
This study was carried out in the Fako and Meme divisions of the South West Region of Cameroon.In each of these divisions, two research sites were selected.The first research site in the Fako division is called the Bonjungo court area.This research site is located at latitude 4.02° N and longitude 9.19° E (Figure 1).The Bonjungo court area has a population of about 11 thousand people.The second site in the Fako division is called Lower Muea.It is located at latitude 41.16 °N and longitude 9.23° E (Figure 1).Lower Muea has a population of about 8 thousand people.Both sites in the Fako division have several similarities and characteristics for which they were selected: (1) they are both located on the foot hills of Mount Cameroon where there are fertile volcanic soils (Dounias et al., 2002;Sevink et al., 2004).(2) They both have an equatorial climate that is hot, humid and has persistent rainfall all year round.They have a mean annual temperature of about 18.6° C and an annual rainfall of about 2815 mm.The driest month is December with about 29 mm of rainfall and the wetest month is August with about 488 mm of rainfall.The warmest month is March with temperatures of about 19.7° C while the coldest month is July with temperatures of about 17.3°C.(3) About 80% of their population is comprised of small-scale farmers and their families who depend entirely on agriculture for their livelihoods.
In the Meme division, the first research site was Bole Bakundu.This research site is located at latitude 4.64° N and longitude 9.73° E (Figure1).Bole Bakundu has a population of about 4 thousand people.The second site in the Meme division is called Marumba one.It is located at latitude 4.63°N and longitude 9.09° E (Figure 1).Marumba one has a population of about 2 thousand people.Both sites in the Meme division have several similarities and characteristics for which they were selected: (1) they are both located on the far eastern slopes of Mount Cameroon where we can find very fertile sandy-loam soils (Dounias et al., 2002;Sevink et al., 2004); (2) they both also have a tropical climate which is hot, humid and with sufficient rainfall; (3) they both record a mean annual temperature of about 25°C and an annual rainfall of about 864 mm.The driest month is January with about 0 mm of rainfall and the wetest month is August with about 247 mm of rainfall.The warmest month is April with a temperature of about 29.3°C, while the coldest month is December with a temperature of about 22.2°C.About 85% of their population is comprised of small-scale farmers and their families who depend entirely on agriculture for their livelihoods.

Data collection
Two focus group discussions (FGDs) were constituted with household heads.One was held in the Bonjungo court area (comprised 8 females and 7 males) and the other was held in Bole Bakundu (comprised 5 females and 10 males).The participants were invited to participate in the discussions through their local chiefs.The discussions were conducted in order to have deeper view points on the relationship between agroecology and conventional techniques of production and crop yields from among small-scale farmers.The discussions were conducted by the researcher and three research assistants.All the objectives of the FGDs were read out and explained to all the participants.In cases where a participant did not understand because of language barriers, research assistants who spoke Mokpwe and Oroko languages (local languages spoken by the Bakweris and Bakundus of the Fako and Meme divisions, respectively) helped in the translation.Participants in the FGDs had to be fulltime small-scale farmers, they had to be involved in food crop cultivation, they had to be willing to permit the researchers to visit their farms and they were free to opt out at any stage of the discussion.The participants were also asked to give reasons for their opinions during the FGDs.
The key themes discussed during the FGDs were: (1) knowledge, use and effects of agroecology techniques on crop yields; (2) knowledge, use and effects of conventional techniques on crop yields.
After the FGDs, a stratified-random one-to-one survey of 200 small-scale farming households was conducted through the administration of 200 questionnaires.Fifty (50) questionnaires were administered in each of the four research sites.However, before the questionnaires were administered, they were pre-tested on three respondents at each research site to ensure that the questions were properly formulated.In cases where questions were ambiguous, these questions were restructured to improve their clarity.The questions concerned the following themes: (1) respondents' age group, gender, level of education, number of years of farming experience, annual income and the number of family members that live and work on the farm; (2) respondents' knowledge and usage of agroecology and conventional farming techniques and how these affect small-scale crop yields.The detailed socio-demographic data collected in this study are shown in Table 1.

Data analysis
The FGDs data were analysed using verbatim transcription and Wordstat 7 context analysis software.Context analysis enhances the identification of key themes emanating from the discussion.The Wordstat 7 software was used because of its ability to identify themes or relationships in verbatim responses, focus group transcripts or other text sources.It involved four main steps (Adam et al. 2015); 1. identification of the themes, 2. attributing codes to the themes, 3. classification of responses under the themes; and 4. integration of themes and responses into narratives.Also, descriptive and inferential statistical tools were used to analyse the quantitatively derived data using SPSS version 20.Specifically, the descriptive statistical methods included: frequencies, means and percentages based on all the questions under investigation.In terms of inferential statistics, the study used essentially correlation coefficients, coefficients of determination and linear regressions models to investigate the relationship between variables.In computing these equations, the most reported yield level of 701-1000 kg per year was used as the dependent yield variable.This was selected based on the population frequencies as a majority of the respondents reported yields within this range with a total frequency of 71 for both agroecology and conventional farming (Table 1).
The equations used to compute the linear regression models were as follows, considering as crop yields in the range of 701 to 1000 kg -1 : where α1Xagro_agr is the agroecology techniques for different age groups; α2Xconv_agr is the conventional techniques for different age groups.
where α1Xagro_edu is the agroecology techniques for different levels of education; α2Xconv_edu is the conventional techniques for different levels of education.
where α1Xagro_fexp is the agroecology techniques for different years of farming experience; α2Xconv_fexp is the conventional techniques for different years of farming experience.
where α1Xagro_income is the agroecology techniques for different income levels; α2Xconv_income is the conventional techniques for different income levels.

𝑌 𝑐
where α1Xagro_nfamily is the agroecology techniques for different numbers of family members that live and work on the farm; α2Xconv_nfamily is the conventional techniques for different numbers of family members that live and work on the farm.

Socio-economic and demographic characteristics of the respondents
Six main characteristics were evaluated (Table 1).Generally, of the 200 small-scale farmers interviewed, 54% (108) were males while 46% (92) were females.
According to most of the respondents, when both men and women or couples are involved, the male in the couple is still considered as the main farmer, because men are considered as the main providers of sustenance to their families, while the women either accompany the men to work on the farm or stay at home to take care of children and perform other housewife chores.This distribution heralds the debate that when it comes to issues of ownership and access to land and its resources, men have both ownership and access while women in most cases may be granted access through the men's discretion.Married women do not have unlimited ownership of land and its resources.In terms of the age groups of small-scale farmers, 63 respondents representing 31% of the total belonged to 40 to 45 years age group while the >60 years age group recorded the smallest number of respondents (9 respondents or 4.5%).
Results from the FGDs show that the >60 years age group was essentially made of the old who are no longer actively capable of carrying out sustained farming as a result of a reduction in their strength.In relation to level of education, the majority of the respondents representing 56% of interviewees had achieved only primary education.The range of 21 to 25 years of farming experience was the lead category with 47 respondents representing 23.5% of the interviewees.In terms of annual income, the <240 thousand FCFA income group recorded the highest level with 45% or 47 respondents.
The generalized observation was that as income levels increased, the number of interviewees decreased.
According to the FGDs, the farmers are generally poor and have limited access to expensive farming inputs.Finally, with regards to the family members who live and work on the farm, the results showed that on average, between 3 and 5 persons live and work on the farm.This was reflected in 45% or 108 of all respondents recorded for this category.From the FGDs, this study observed that in all of the study sites, family labour is a key production element as often the farmers depend on the assistance they obtain from their wives and children in order to be able to meet production needs.The ranges presented here include all those living and working on the farm including the farmers, their wives, children, and other dependents.

Evolution of current and past yields
According to population perceptions, current arable crop production yields (December, 2014) in the four study sites are declining.This is evident as of the 200 respondents, 178 respondents said that their current yields were declining, while 8 said they were stagnant, 14 said they were increasing, 0 said they are unpredictable, 0 said they have no idea and 0 said others.In terms of yields during the past decade, 154 respondents said they were increasing while 46 said they were declining.The majority of the respondents representing 71 persons had crop yields between 701 and 1000 kg year -1 (Figure 2a,  b, and c).These results presented here underscore the fact that current small-scale farming crop yields are lower than they were 10 years ago.These results are consistent with the FGDs.

Agroecology and conventional yields, scatter plots, correlation coefficients and coefficients of determination
It was observed that the 41 to 45 years age group had the highest number of 25 respondents representing 35.21% of respondents having their yields comprised between 701 and 1000 kg year -1 (Figure 3a).As presented earlier, 71 respondents have yields of between 701 and 1000 kg per year.When it comes to how this trends for different age groups, it can be observed that the 41 to 45 years age group has the highest number of respondents of 25 (Figure 3a).The latter age group represents the active age group and this may be the reason why this age group dominates the maximum yield range. Results from the FGDs also go a long way to confirm this assertion.
In terms of the contributions of agroecology and conventional farming to crop yields in the four study sites, the following observations were made.A scatter plot of the number of respondents in different age groups that use agroecology techniques against the number of respondents with yields comprised between 701 and 1000 kg per year showed a perfect positive relationship.
This was expressed by a correlation coefficient (R) of 0.98 and a coefficient of determination (R 2 ) of 0.96 (Figure 3b).These values are higher than0.96and 0.92 obtained from the scatter plot of the number of respondents in different age groups that use conventional techniques against the number of respondents with yields of between 701 and 1000 kg per year (Figure 3c and Table 2).Consequently, these results showed that the yields from the four study sites were higher under agroecological than conventional techniques for different age groups.The higher correlation coefficient depicted a more positive relationship between the number of respondents that use agroecological techniques for different age groups and yields while the higher coefficient of determination for agroecology techniques also means that about 96% of the changes in yields can be explained by the linear relationship between yields and agroecology techniques.For conventional farming techniques, the coefficient of determination was about  92% and inferior to 0.96 obtained for agroecological techniques.Therefore, statistically, it is concluded that agroecology techniques are more responsible for yield increases than conventional farming techniques for the variables presented.
In the area of level of education, this study also found that of the 71 respondents with yields that comprised between 701 and 1000 kg, the majority of 31 have only attained primary education (Figure 4a).This goes to support the view from the FGDs that a majority of the respondents in the study sites have not studied beyond the primary level of education.The scatter plots representing the relationship between the number in different education levels involved in agroecological techniques and the number with the yield range of 701 and 1000 kg year -1 (Figure 4b) are more responsible for increases in crop yields than the scatter plots between the number in different levels of education that use conventional techniques and the number with yields between 701 and 1000 kg year -1 (Figure 4c and Table 2).This is observed as the agroecology yield scatter plot for different levels of education has a correlation coefficient of 0.98 and a coefficient of determination of 0.97 (Figure 4b).These were higher than the correlation coefficient and coefficient of determination for the relationship between yields and conventional farming that were 0.98 and 0.96, respectively (Figure 3c and Table 2).Again, it is observed that, for different levels of education, agroecology techniques seem to be more associated to yield increases.
Out of the 71 respondents that have yields of between 701 and 1000 kg year-1 , the highest category of 23 respondents had between 21 and 25 years of farming experience (Figure 5a).Here also, agroecological techniques were seen to affect yields in a positive direction for different numbers of years of farming experience than for conventional techniques of farming (Figure 5b and c and Table 2).
In terms of income levels, the respondents, in their majority, were generally poor with most of them belonging to the annual income level category of <240 thousand FCFA year -1 (Figure 6a).Unlike with the other socioeconomic variables, the scatter plot of number in different income groups that use agroecological techniques and the number of respondents with yields between 701 and 1000 kg year -1 had a correlation coefficient of 0.99 and an inferior coefficient of determination of 0.97 (Figure 6b) when compared with a correlation coefficient of 0.99 and a coefficient ofdetermination of 0.98 for the relationship between number in different income groups that use conventional techniques and number with yields of between 701 and 1000 kg year -1 (Figure 6c and Table 2).The implication here is that, when it concerns different income groups, conventional farming systems seem to increase yieldsslightly more than agroecology systems.
Concerning the number of family members that live and work on the farm, this study has found that, for all four study sites and with respect to the 71 respondents that had yields of between 701 and 1000 kg per year (Figure 7a), a majority of 35 belong to the group of 3 to 5 persons that live and work on the farm.In terms of the relative contributions of agroecology and conventional farming to yields, the yields under conventional farming techniques are more important than those under agroecology techniques.The conventional farming scatter plot has a coefficient of determination of 0.98 (Figure 7b and c and Table 2) that was statistically higher than that of agroecology farming.

Multiple linear regression models
Different linear regression models computed based on the different socio-economic variables under study tended to be very consistent with the     results from the scatter plots, the correlation coefficients and the coefficients of determination.In all of these models, yields constitute the dependent variable and the number of respondents that use agroecology and conventional techniques constitute the independent variables.For example, the regression model that explains the predictors of yield based on different income groups showed that the number of respondents that use agroecology techniques had a t-value of 2.71 and a pvalue of 0.04.These values were different from the tvalue of 1.0 and p-value of 0.36 for the number of respondents that use conventional techniques.The higher t-value of 2.71 for agroecology shows that agroecology contributes more to yields than conventional farming.The parallel p-value of 0.04 is the lower of the two and depicts the fact that the likelihood of having outcomes that are different from the current is highly limited and thus, these results are reliable (Table 3).For the effects of agroecology and conventional techniques on yields based on: levels of education (Table 4), number of years of farming experience (Table 5) the results were consistent as agroecology techniques were seen to influence yields more due to larger t-values and smaller p-values.
However, in the case of the effects of agroecological and conventional techniques on yields based on income levels and the number of family members that live and work on the farm, this study observed that conventional farming techniques were dominant.For example, in the case of different income groups, conventional farming techniques had a higher t-value of 0.76 and a lower pvalue of 0.50 (Table 6) meaning that conventional farming techniques affect yields more than agroecological techniques.This again can be explained by the fact that conventional techniques or inputs were not free and need to be purchased and that was why, when income increases among respondents, the rates of usage tended to be high.Also, for the final model based on the number of family members that live and work on the farm, this study also found out that, conventional farming techniques tended to affect yields more than agroecology as seen in a higher p-value of 1.31 and a lower t-value of 0.41 for conventional farming when compared with agroecological practices (Table 7).

DISCUSSION
With the exception of socio-demographic variables like income levels and the number of family members that live and work on the farm, for all the other social demographic variables and characteristics, crop yields tended to increase more positively under systems of agroecology than conventional farming.Income levels tended to increase yields more under conventional farming techniques because most conventional inputs are not freely available and farmers who are richer are often able to purchase inputs such as inorganic fertilizers, hydrides, machines, etc. Agroecology farming systems on the other hand were not highly income driven as is the case with conventional inputs because they are often freely available from the environment (Nkamleu and Adesina, 2000;Palm et al., 2001;Negi, 2014).In general, agroecology seems to be generally more favorable but again, both techniques, that is, agroecology and conventional techniques are very highly rated with correlation coefficients and coefficients of determination generally above 70%.This result is consistent with recent findings from across Africa that showed that both agroecology and conventional farming techniques are generally being used in Africa.However, the problem with agroecology is that it is still unable to support high yields and this is linked to the fact that this system of production has not been properly valorised in most African countries and many farmers still simply farm under natural conditions without taking into account agroecological practices.
These findings are consistent with several other studies on the effects of agroecology and conventional farming on yields.It has been suggested that agroecology techniques alone are unable to trigger sufficient yields if they are not associated with conventional techniques (Chivenge et al., 2009).Using organic fertilizers as an example of agroecology techniques, it has been further substantiated that the current levels of organic fertilizers usage in most of Africa cannot support an increase in production beyond levels at which galloping populations can be fed (Borlaug, 2000;Tilman et al., 2002;Ayuke et al., 2011).Epule et al. (2015) argued that organic fertilizers can increase production to a certain threshold beyond which further increase can only be achieved through the wise application of inorganic fertilizers.The best scenario for maximum yields is when organic inputs such as manure are combined with inorganic inputs such as nitrogen, potassium and calcium fertilizers (NPK) (Pichot et al., 1981;Bationo and Mokwunye, 1991;Bado et al., 1997;Adesina et al., 2000;Bado et al., 2007;Folefack, 2009).To further support this, in the North West of Ethiopia, Demelash et al. (2014) present findings from an entirely experimental approach in which a combination of 6 t compost ha -1 year -1 and 34.5 to 10 kg N-P ha/year produced the highest crop yields of about 521% when compared with other scenarios that had either only organic or inorganic inputs.
The observation that agroecology techniques were dominantly used by the respondents interviewed in this study was also consistent with other previous studies.For example, organic fertilizers are freely available and can be easily obtained from the environment.In poor communities in Africa in general and Cameroon, particularly, where access to conventional inputs is often limited by purchasing power, organic resources remain the critical nutrient sources for most small scale-farmers who are generally unable to secure large quantities of inorganic inputs (Rosegrant and Svendsen, 1993;Matson et al., 1997;Hossain and Singh, 2000;Reid et al., 2003;Lindell et al., 2010a, b;Palm et al., 2010;Godfray et al., 2010;Epule et al., 2012).One major problem of food crop production in Cameroon is related to inadequate access to fertilizers and high yielding varieties and poor water and soil management (Henao and Baanante, 2006).This observation explains why agroecology techniques that promote natural systems interactions and inputs are more dominant in the study sites than conventional techniques.The problem is that, agroecology systems in Cameroon have not been properly valorised to levels where small-scale farmers can obtain maximum benefits from their use.Most of the systems described as agroecology in this study are actually natural as in most cases the farmers simply clear up the land and then plant the crops without any additional inputs.In most cases, the use of agroecology techniques is accidental as most farmers tend to use the latter because of lack of access to conventional techniques.The advantages of agroecology techniques are not being fully experienced by farmers because the scale of utilization and level of valorization are low.The farmers need to be educated on the various ways of valorising agroecology inputs such as: the establishment of pilot agroecology farms; usage of various natural substances such as grass and food wastes to produce compost and organic manure; water and soil management; the use of prey-predator relationships and intensified usage of animal droppings and urine to control pests instead of using lethal pesticides (Epule et al., 2015).
Agroecology inputs such as compost and manure are capable of supplying the much needed crop nutrients and are more accessible because they are free and they are not sources of stream and river pollution as it is observed with inorganic or mineral fertilizers (Lindell et al., 2010a, b;Dubois, 2011;Krawinkel, 2012).As such, well valorised organic farming systems will go a long way to enhance soil fertility, biodiversity, soil organic carbon and nitrogen (Liu et al., 2009).
In the midst of the advantages of organic fertilizers, many studies have argued that in terms of the net effects of optimally managed organic or agroecology farms on yields, organic farms produce lower yields per unit area when compared with inorganic or conventional farms (Lal, 2006).These findings were later confirmed by Seufert et al. (2012) after Badgley et al. (2007) argued that organic or agroecology farming systems are more productive than conventional farms, "...organic agriculture has the potential to contribute quite substantially to the global food supply, while reducing the detrimental environmental impacts of conventional agriculture".In fact, Seufert et al. (2012) criticised Badgley et al. (2007) findings on the following grounds: (1) It included organic crop yields from farms experiencing large inputs of nitrogen from manure; (2) They used less representative low conventional yields; (3) They failed to consider yield reductions over time due to rotations with non-food crops; (4) Double counting of high organic yields; and (5) Extensive use of unverifiable data from grey literature sources and equal weighting of the latter with more rigorous studies.A major dilemma in the current status of agroecology is whether it will solve the twin problems of providing more food to mankind and at the same time minimising environmental foot prints.This is because the Badgley et al. (2007) study that asserts increase in food production under agroecological conditions has been criticised on grounds of double counting and for not adequately differentiating between agroecological and conventional farms.The Seufert et al. (2012) also fails to deliver by not including any comparisons between conventional and organic yields in Africa as the meta analysis focuses on North America, Central America, Western Europe, and parts of South East Asia.It is more appropriate to say there are inadequate studies on this topic in Africa.Generally, the Seufert et al. (2012) paper takes on a more pessimistic perspective on the prospects of organic systems.However, recent studies based on a synthesis of the existing literature on the use of agroecology and conventional farming methods in Africa by Kearney et al. (2012) and Epule et al. (2015) argue that organic farming can only support yields to a certain threshold beyond which further increase in yields can only be attained through the application of inorganic fertilizers.

Conclusion
This study has found that agroecology techniques of farming seem to contribute more to crop yields among small scale peasant farmers in the four study sites.However, exceptions exist when scenarios of the effects of income and the number of family members that live and work on the farm are concerned.Here, conventional farming techniques seem to contribute more to yields.Generally, the performance of both techniques is higher than 70% with agroecology related techniques being slightly dominant.The key problem facing agroecology related techniques now is the low level of valorisation.Observations and FGDs show that, what is currently termed agroecology in most of the study sites is simply production under natural condition without a clear valorisation of composts, organic manure, prey-predator relationships inter alia.This study defines valorisation of agroecology as the complete package of techniques related to not only adding value to different agroecology options like manure, compost, animal droppings and plant residues but also training the farmers on the different ways by which they can obtain and increase their use of these elements at little or no costs.Therefore, through training and capacity building of the small scale peasant farmers on the various ways of obtaining and using agroecology inputs, valorisation can be achieved.In the main time, both conventional and agroecology techniques should be used in the study sites to assure maximum yields.
For issues such as the usage levels of agroecology and conventional farming methods, the reasons for the adoption of the different methods of agroecology and conventional methods and various coping mechanisms are all worth verifying further.Farmers using mainly conventional techniques could be persuaded to integrate progressively certain agroecological techniques primarily because this has the ability of lowering the negative externalities (e.g. on the environment including water resources) associated with dominantly conventional techniques.One major way of encouraging this might be to build on people's community solidarity especially if more farmers can be convinced that doing this would have positive effects on their own families and neighbours' families.

Figure 1 .
Figure 1.Location of four study sites in the South West Region of Cameroon.

Figure 2 .
Figure 2. (a) Respondents perception of the status and evolution of current crop yields.(b) Status and evolution of past crop yields in the last decade.(c) Actual current mean annual crop yields in kilograms based on population perceptions of 200 respondents in all four study sites.

Figure 3 .
Figure 3. (a) Evolution of both agroecology and conventional yields for different age groups.(b) Scatter plots of maximum yields and age groups of farmers under agroecology techniques.(c) Scatter plots of maximum yields and age groups of farmers under conventional techniques.

Figure 4 .
Figure 4. (a) Evolution of both agroecology and conventional yields for different levels of education.(b) Scatter plots of maximum yields and level of education of farmers under agroecology techniques.(c) Scatter plots of maximum yields and level of education of farmers under conventional techniques.

Figure 5 .
Figure 5. (a) Evolution of both agroecology and conventional yields for different number of years of farming experience.(b) Scatter plots of maximum yields and years of farming experience of farmers under agroecology techniques.(c) Scatter plots of maximum yields and years of farming experience of farmers under conventional techniques.

Figure 6 .
Figure 6.(a) Evolution of both agroecology and conventional yields for different annual levels of income.(b) Scatter plots of maximum yields and annual income of farmers under agroecology techniques.(c) Scatter plots of maximum yields and annual income of farmers under conventional techniques.

Figure 7 .
Figure 7. (a) Evolution of both agroecology and conventional yields for numbers of family members the live and work on the farm.(b) Scatter plots of maximum yields and numbers of family members that live and work on the farm for farmers under agroecology techniques.(c) Scatter plots of maximum yields and numbers of family members that live and work on the farm for farmers under conventional techniques.

Table 1 .
Socioeconomic and demographic characteristics of respondents.

Table 2 .
Correlation coefficients (R) and coefficients of determination (R 2 ) between yield range with highest number of respondents and selected variables.
**Positive and dominant relationship, *Positive but less dominant relationship.

Table 3 .
Regression model of the predictors of mean annual yields based on different age groups.

Table 4 .
Regression model of the predictors of mean annual yields based on different levels of education.

Table 5 .
Regression model of the predictors of mean annual yields based on number of years of farming experience.

Table 6 .
Regression model of the predictors of mean annual yields based on different income levels.

Table 7 .
Regression model of the predictors of mean annual yields based on the number of family members that live and work on the farm.