ECONOMICS OF CONTROL METHODS FOR WITCHWEED, STRIGA HERMONTHICA IN THE NORTHERN GUINEA SAVANNA OF NIGERIA
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ABSTRACT
Witchweed, Striga hermonthica (Del.) Benth is a major threat to the realization of yield potentials of cereal crops especially maize. Yield losses due to Striga in the Northern Guinea savanna of Nigeria have also impacted negatively on the livelihood of over 60% of the farming population. Various control methods have been developed but the level of use or adoption by farmers has been constrained by inadequate information on the economic performance of these methods. This study was therefore designed to compare resource use levels, grain yield, effectiveness (Striga counts) and the economic performance of five Striga control methods. The methods were: the cultivation of a Striga tolerant maize variety (Acr 97 TZL COMP.1–W) followed by Striga tolerant maize variety in the second year (T1), cultivation of an improved soyabean variety (TGX 1448-2E)
followed by Striga tolerant maize variety in the second year (T2 ), cultivation of improved
soyabean variety followed by a local maize cultivar in year two (T3), cultivation of a local maize
cultivar with a high level of Nitrogen fertilization for two consecutive years (T4) and farmers
practice for Striga control (T5). The study also determined the optimum Striga control plan and the
impact of simulating the scarcity of improved maize and soyabean seed varieties, farm land and NPK fertilizer on the optimum plan. Data were obtained from an on-farm trial conducted on 12 farmers’ fields with uniform infestations of Striga hermonthica at Kugu and Dambo villages in the Northern Guinea savanna of Nigeria, during the 2005/2006 and 2006/2007 cropping seasons. The tools of analyses employed for the study included Analysis of Variance for a randomized complete block design, Partial Budgeting, Marginal Rate of Return, Benefiit-Cost Ratio, Dominance
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Analysis, Linear Programming (LP) Model and five variant models of the basic LP model. A Pair Wise Matrix Ranking Technique was also used to identify the constraints to the use of the Striga control methods.
The results obtained revealed that the seed use levels for plots T1 and T2 were significantly lower
(p<0.01) than that of the other treatments plots. Furthermore there was no significant difference (p<0.01) between the fertilizer use levels for treatments plots T1, T2, T3 and T5 with the exception
of T4. Striga control was significantly higher (lower Striga counts) in T2 and T3 plots compared to
other treatments plots. This was followed by the Striga counts for T1 plots. However, Striga
control was significantly lower (higher Striga counts) in T5 plots compared to plots T1 and T4. The
maize grain yields were significantly higher (p<0.01) in treatments plots T1 and T2 compared to
other plots. Further analysis showed that T1 had a higher cumulative gross margin per hectare
(N76, 884.61) followed by T2 (N36, 287.00). The cost-benefit ratio was higher for T1 (2.27)
followed by T2 (1.58). The marginal rate of return was also higher for T1 (N885.00) followed by T2
(N8.90). These results and the dominance analysis clearly revealed the economic superiority of T1
and T2 over the other treatments.
The LP result suggests that cultivation of Striga tolerant maize variety followed by a Striga tolerant maize variety in the second year (T1) was the optimum Striga control method. The result
of variant model ‘A’ which simulated the scarcity of Striga tolerant maize variety suggests that improved soyabean variety followed by a local maize cultivar (T3) was the optimum Striga control
method. In the case of variant model ‘B’ which simulated the scarcity of improved soyabean
variety, treatment T1 re-emerged as the optimal control method. The optimal control method for
variant model ‘C’ which simulated the combined scarcity of both Striga tolerant maize and improved soyabean varieties was T5 (farmers practice). The result of variant model ‘D’ which
simulated land abandonment as a result of Striga infestation, revealed that treatment T1 was the
optimum Striga control method but with a 43% reduction in the amount of capital invested in maize production. The result of Variant model ‘E’ which simulated the scarcity of NPK fertilizer revealed that none of the treatments was competitive enough to be an optimal control method for
Striga. High costs of fertilizer and scarcity of improved maize and soyabean varieties were identified as the major constraints to the use of the Striga control methods by farmers. In conclusion, Striga infested maize fields can be put to profitable use with the continuous cultivation of Striga tolerant maize variety or improved soyabean variety followed by a Striga tolerant maize variety in the second year. The study suggests that policies that will ensure timely and sufficient supply of Striga tolerant maize and soyabean varieties, fertilizers and agricultural credit to farmers should be addressed by government in order to achieve a long run Striga control in the study area and the country at large.
| CHAPTER ONE: INTRODUCTION | ||
| 1.1 Background to the Study | 1 | |
| 1.2 | Economic importance of Striga | 2 |
| 1.3 | Statement of the Problem | 5 |
| 1.4 | Objectives of the Study | 8 |
| 1.5 | Justification for the Study | 8 |
| CHAPTER TWO: LITERATURE REVIEW | ||
| 2.1 | Description and Occurrence of Striga | 10 |
| 2.1.1 Seed Production and Germination Requirements | 10 | |
| 9 | ||
| 2.1.2 Seed Longetivity | 10 |
| 2.1.3 Seed Dispersal | 10 |
| 2.1.4 Striga Infestation | 11 |
- Empirical Studies on Striga Infestation, Control and Adoption of Control
| Measures | 13 | |
| 2.2.1 Effects of Nitrogenous Fertilizers on Striga Infestation and Control | 14 | |
| 2.2.2 Effects of Hand Pulling on Striga Infestation and Control | 15 | |
| 2.2.3 Effects of Chemicals on Striga Infestation and Control | 17 | |
| 2.2.4 Empirical Studies on Integrated Striga Control Methods | 21 | |
| 2.2.5 Empirical studies on the adoption of Striga control methods | 22 | |
| 2.3 | Definition of ‘New’ Technology and their Evaluation at the Farm Level | 23 |
| 2.4 | Methods of Evaluation | 26 |
| 2.4.1 | Partial Budgets | 27 |
| 2.4.2 | Gross Margin Analysis and Budgeting | 28 |
| 2.4.3 | Dominance Analysis | 28 |
| 2.4.4 | Marginal Rate of Return | 29 |
| 2.4.5 | Cost- Benefit Ratio | 30 |
| 2.4.6 | Linear Programming | 30 |
| 2.5 | Aids to Weed Management Decisions | 35 |
| 2.6 | Empirical Studies on the Economics of Weed Control | 37 |
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| 2.7 | Review of Studies on the Use of Linear Programming | 39 |
| CHAPTER THREE: METHODOLOGY | ||
| 3.1 | Description of the Study Area | 42 |
| 3.2 | Selection of Participating Farmers and Data Collection | 43 |
| 3.3 | Analytical Techniques | 45 |
| 3.4 | Description of Activities and Constraints in the LP Model | 51 |
| 3.4.1 | Activities in the LP Model | 51 |
| 3.4.2 | Restrictions in the LP Model | 52 |
| 3.5 | Limitations of the Study | 55 |
| CHAPTER FOUR: RESULTS AND DISCUSSIONS | ||
| 4.1 | Input-Output Levels of Alternative Striga Control Methods | 57 |
| 4.1.1 | Input-Output Levels of Alternative Striga Control Methods (2005) | 57 |
| 4.1.2 | Striga Counts for Alternative Striga Control Methods (2005) | 59 |
| 4.1.3 | Input-Output Levels for Alternative Striga Control Methods (2006) | 60 |
| 4.1.4 | Striga Counts for Alternative Striga Control Methods (2006) | 61 |
| 4.2 | Costs and Returns of Alternative Striga Control Methods | 62 |
| 4.2.1 | Partial Budgeting, Cost-Benefit Ratio, Marginal and Dominance Analyses 62 | |
| 4.3 | Results of the Linear Programming Analysis | 67 |
| 4.3.1 | The Optimum Striga Control Method of the Basic Model | 71 |
| 4.3.2 | Variant Model A: Optimum Striga Control Method when the buying | |
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| activity for Improved Maize Seed is excluded from the Basic model | 74 |
| 4.3.3 Variant Model B: Optimum Striga Control Method when Buying Activity | |
| for TGX 1448 is Eliminated from the Basic Model | 77 |
| 4.3.4 Variant Model C: Optimum Striga Control Method when the Buying | |
| Activities for ACR 97 and TGX 1448 are Eliminated from the Basic | |
| Model | 79 |
| 4.3.5 Variant Model D: Optimum Solution when there is a reduction in the | |
| average Farm Size of the Basic Model | 82 |
| 4.3.6 Variant Model E: Optimum Farm Plan when the Buying Activity for NPK | |
| Fertilizer is Eliminated from the Basic Model. | 84 |
| 4.4 Constraints to the Use of Striga Control Methods | 86 |
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS
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| 5.1 Summary of Major Findings | 89 |
71
5.1 Summary
| 71 | ||
| 5.2 | Conclusions | 90 |
| 5.3 | Recommendations | 91 |
| 5.4 | Suggestions for Further Studies | 92 |
| REFERENCES | 94 | |
| APPENDICES | 108 | |
| LIST OF TABLES | |
| Table 1 Lay Out of Farmer’s Fields | 45 |
| Table 2 Household Composition and Derived Labour | 53 |
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| Table 3 Derivation of Household Consumption Unit | 55 | ||
| Table 4 Input-Output Levels and Striga Counts for Striga Control Methods | |||
| kg/25m2 (2005) | 58 | ||
| Table 5 Input-Output Levels and Striga Counts for Striga Control Methods | |||
| kg/25m2 (2006) | 60 | ||
| Table 6 | Cummulative Partial Budgeting Analysis (2005/2006) | 64 | |
| Table 7 | Benefit-Cost Analysis for Striga Control Methods | 65 | |
| Table 8 | Marginal analysis for Striga Control Methods | 65 | |
| Table 9 | Dominance Analysis for Striga Control Methods | 66 | |
| Table 10 The Basic Model of the Linear Programming Analysis | 68 | ||
| Table 11 Summary of the Optimal Farm Plan/ Existing Activity Level for the | 72 | ||
| Basic Model | |||
| Table 12 | Resource Use Levels in the Optimum Plan of the Basic Model | 74 | |
| Table 13 | Summary of the Optimal Farm Plan/ Existing activity Level for Variant | ||
| Model A | 75 | ||
| Table 14Resource Use Levels in the Optimum Plan of Variant Model A | 77 | ||
| Table 15 | Summary of the Optimal Farm Plan/ Existing activity Level for the | ||
| Variant Model B | 78 | ||
| Table 16 | Resource Use Levels in the Optimum Plan of Variant Model B | 79 | |
| Table 17 Summary of the Optimal Farm Plan/ Existing activity Level for the | |
| Variant Model C | 80 |
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| Table 18 | Resource use Levels in the Optimum Plan of Variant Model C | 81 |
| Table 19 | Summary of the Optimal Farm Plan/ Existing Activity Level for the | |
| Variant Model D | 83 | |
| Table 20 | Resource Use Levels in the Optimum Plan of Variant Model D | 84 |
Table 21 Summary of the Optimal Farm Plan/ Existing Activity Level for the Variant Model E 85
Table 22 Pair Wise Matrix Comparison of Constraints to the Use of Striga
| Control Methods | 86 |
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| LIST OF FIGURES | ||
| Figure 1 | Life Cycle of Striga hermonthica | 13 |
| Figure 2 | A Shift in the Supply Curve brought about by Adoption of a New | |
| Technology | 25 | |
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| LIST OF APPENDICES | ||
| Appendix 1 | Log Book | 108 |
| Appendix 2 | Genstat Output | 114 |
CHAPTER ONE
INTRODUCTION
- Background to the Study
The control of weeds has always been one of the greatest resource-consuming operations in crop production. In addition to requiring effective control measures, weeds rob crop plants of nutrients and water, often serve as hosts to insects and other pests, and create problems in harvesting and processing. The use of herbicides has enabled farmers to control weeds with greater ease and to free crops from competition with weeds for nutrients, light and water at critical periods of their growth cycle (Abu-Hamdeh, 2003). There has however been a growing apprehension among ecologists/ environmentalists about the use of chemicals in general and herbicides, in particular, as it is feared that such use poses a serious
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hazards to human health and that they pollute and poison soils and ground water (Stonehouse et al., 1996).
Striga is one of the very few flowering plants that are parasitic on other plants, dodder and mistletoe being other examples. Striga has been given the common name of “witchweed” because it attaches itself to the roots of the host plant thus depriving it (the host) of water and nutrients. Two species of Striga, Striga hermonthica and Striga asiatica attack sorghum, millet, and maize while another species, Striga gesneroides, is specific to cowpea (Ramaiah, et al., 1983). Depending upon the extent of infestation, reductions in grain yield of 30-60% are common. Practical control methods consist of a combination of crop rotation with non-hosts, weeding, and use of resistant varieties. It produces a large number of seeds which can remain dormant but viable for many years, therefore once Striga becomes established in a field, eradication is very difficult (Kim 1991; AATF 2006). De Groote et al. (2005) opined that Striga is a particular problem in areas with low moisture and where soil fertility is being eroded through increased population pressure, decreased use of fallow and minimal use of organic or inorganic fertilizer. Most importantly, it mostly affects the livelihoods of poor subsistence farmers in cereal-based agricultural systems in Africa (Weed Busters 2003).
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1.2 Economic Importance of Striga
Striga species have taken root throughout the continents of Africa and Asia, imparting extensive damage to staple cereal crops. Mboob (1989) reported that an annual loss of between US$28 million and US$12.4 billion in crop revenue is caused by Striga in West Africa. Two thirds of the 73 million hectares of cropland planted to cereal crops in Africa is also said to be threatened by Striga species (Lagoke et al., 1991). A study by Sauerborn (1991) estimated that Striga, causes an annual grain yield loss of 4.1 million metric tonnes and infests 21 million hectares of cereal cropland in Africa. The greatest damage from Striga infestation occurs in the sahelian and savanna zones of Africa, where nearly 100 million inhabitants depend on maize, sorghum, millet, and cowpea as staple foods (Lagoke et al., 1991). Under artificial infestation at different levels, in Nigeria, yield loss for the tolerant varieties varied between 27% (at 2250 Striga seeds/hill) to 35% (at 4500 seeds/hill), for the susceptible varieties yield loss ranged from 43% (at 750 seeds/hill) to 74% (at 3750 seeds/hill) (Kim and Adetimirin 1997).
Studies in West Africa compared maize yields of susceptible and tolerant varieties in fields under natural Striga infestation with yields of the same varieties in non-infested fields (Kim et al., 2002). In the savannahs of Nigeria, yield reduction in the tolerant varieties of 31% was found, in the susceptible varieties 62% (1985 trials). Trials in Cameroon in the same year produced lower estimates: 21% for the tolerant
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varieties, and 41% for the susceptible varieties. Estimated yield losses due to Striga from on-farm studies and conservative estimates are reported to be 40% of total maize production for all of Africa (US $7 billion), ranging from 20-95% in East Africa, 20-35% in Gambia, 10-90% with an average of 35% in Nigeria (US$5-6 million). Kroschel (1999) opined that Striga also causes indirect production losses when farmers change production strategies to respond to heavy infestations. For example, farms in Striga endemic areas have often been subjected to long fallow periods of up to 15 years. Some have been totally abandoned while in extreme cases human migrations out of heavily infested areas have occurred.
Similarly Siegfried (1994) reported that Striga infestation is also of concern to farmers because of its direct effects on the labour supply patterns of their households. When Striga attacks millet-cowpea, sorghum-cowpea or other crops for which women have direct responsibility for example, the Striga-specific weeding adds an additional burden to those of water collection, food preparation and other chores traditionally reserved for women in the West African sub region. Weed Busters (2003) also reported that Striga has impacted negatively on the livelihood of about 60% of the farming population as over 70% of the farmlands usually put to maize production in Northern Nigeria have also been abandoned or substituted with non host crops such as cowpea, soyabean and groundnut. The situation is more serious in the semi-arid regions where crops are already under moisture and nutrient
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stress. Striga has also infested over 2.5 million hectares of land. This biological invasion is a leading cause of food insecurity and rural stagnation in Africa (AATF, 2006). Striga hermonthica the most important parasitic weed species on a world scale is thus an economically important constraint to cereal production in much of Africa (Mullen, 1999).
1.3 Statement of the Problem
Cereal crops, especially maize, are the most important food crops cultivated in Nigeria, with a high yield potential in the savannas (Lagoke et al., 1991) and is consumed by more than 70% of the population (IITA, 2004). Due to dwindling land resources, soil fertility problems and weed build up, the horizontal increase in crop output is becoming difficult day by day. In these circumstances, the only way to have more production is vertical increase i.e. in output per unit of land area (Khan et al., 2000). However, there are many constraints that i