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Climate change episodes are increasingly complicating resource use, access and management in the majority of the developing countries. Parasitic weeds and crop diseases are hurting annual cereal yields. Application of agrochemicals to contain locusts, birds and insects that destroy produce have the propensity to kill pollinators such as bees. Essentially, pollinators play a critical role in ensuring ecological sustainability and food security. The study uses long-term historical data (1961 and 2017) to link climate change, pollinators and cereal yields in Kenya on a multivariate model. The findings revealed that a unit increase in the amount of rainfall will result in a proportionate increase in cereal yields but a unit increase in temperature will lead to varied increases in cereal yields. The findings also revealed that bees played a critical role in the pollination of maize, wheat and beans but not rice. It is recommended that future studies should consider monthly or quarterly climate data in determining future impacts of climate change and pollinators on cereal yields.

Nearly three decades after the formation of the United Nations Framework Convention on Climate Change (UNFCCC) in 1992 with the central aim of addressing climate change, progress has been made in containing the global challenge. Despite the progress, the climate change phenomena continue to escalate and are man’s major threat multiplier, mainly in developing countries (Watts et al., 2018) [

The recent wave of invasive locusts in 2019/2020 destroyed rangelands, led to exacerbation of climate change related crop diseases, intensified human-animal conflicts, and spread of opportunistic weeds that hurt quality and quantity of livestock, fisheries and crop yields (Yanda & Mubaya, 2011) [

Bees are the major pollinators and play a critical role in ensuring ecological sustainability and food security. Worryingly though, efforts to contain destructive swarms of locusts by aerial spraying are also killing important communities of pollinators. Reliable lines of evidence suggest that the mean annual rainfall and temperature will continue to escalate and that this will be characterized by catastrophic environmental, health and economic risks (Cuni-Sanchez et al., 2018 [

If farmers know in time that it will not rain during a given planting season, they can adjust their planting dates and avoid losses. However, unreliable weather information impels them to count loses (ACRE Africa, 2019 [

Certainly, the influence of climate change on maize production in Kenya has been explored (e.g. Hansen & Indeje, 2004 [

The rest of the paper is organised as follows: Section two presents the literature review, Section three details the methodology―under which the empirical model is specified. Section four provides data and variables used in the study while Section five presents the results and discussion. Section six details conclusions and recommendations. At the tail-end, authors' declaration of no conflict of interest is provided.

Various lines of literature reveal that climate change and variability is a problem with direct and indirect ramifications on agriculture (e.g. Kabubo-Mariara & Kabara, 2018 [

It is projected that continued manifestation of climate change will add nearly 600 million people to the 815 million people that are chronically undernourished and worsen water accessibility for 1.8 billion people by 2080 (Kabubo-Mariara & Mulwa, 2019 [

Across the globe, Wuebbles, Chitkara and Matheny (2014) [

A micro-perspective on the viability of adaption to climate change as a driver for food security reveals that adaptation increases food security and that if households affected by the harsh effects of climate change would adapt, a lot would be gained and losses minimized. Interventions that can provide farmers with extension services and access to finance, will enable farmers to overcome constraining household characteristics and incentivize their uptake of the tested adaptation options (Di-Falco, Veronesi & Yesuf, 2011) [

Bloschl and Grayson (2001) observe that interpolation as a technique involves data filtering and change of scale [

Estimating stochastic data may not be feasible without worrying about margins of errors (Bloschl & Grayson, 2001) [

This study adopted the Alvi and Jamil (2018) [

Y t = e β t ( ∏ j = 1 k x j k γ j ) e u t (1)

where, Y t is the quantity of a given cereal yield in time t, x j t a set of climate estimate of temperature and other sets of parameters under estimation, whereby x j is a series of x k elements such that x j = ( x 1 , x 2 , x 3 , ⋯ , x k ) . In addition, γ are parameters whose composition of X ′ j t s . Theoretical literature strengthens the idea that climate change leads to risk exposure that consequentially affects cereal yields. It also reveals that pollinators play a critical role in food security but that they are also affected by climate change. Instrumentally, it is assumed that pollinators have a dual outcome on food security (depending on how climate change influences on bees). These two probable scenarios are captured as shown:

Y t H = e β t H ( ∏ j = 1 k X j k γ j H ) e u i t H (2)

Y t N = e β t N ( ∏ j = 1 k X j k γ j N ) e u t N (3)

where, Y t H and Y t N are cereal yields per hectare over time t accounting for the valuable contribution of pollinators or lack of it, respectively. By taking logs on Equations (2) and (3), we respectively have:

y t H = β t H + X ′ j t γ j H + u t H (4)

y t N = β t N + X ′ j t γ j N + u t N (5)

where, y t H and y t N are the log(s) of given cereal yields per hectare over time t, X ′ j t is log of the inputs. The gains from pollinators on a given cereal yield per hectare are given as a difference of potential gains and losses on yields as:

B t = y t H − y t N = β t H − β t N + X ′ j t ( γ j H − γ j N ) + u t H − u t N (6)

where, B i t is the cereal yields when pollinators aid increase yields; yields under the two scenarios can be compared by:

h t = 1 ; i f y t H > y t N (7)

h t = 0 ; i f y t H > y t N (8)

Above Equations (7) and (8) provide a classical case when the influence of pollinators on yields is positive and negative, respectively. It is assumed that if pollinators are impaired due to climate change, h t = 0 , then s/he misses the comparative advantage that comes with pollinators i.e. h t = 1 . In order with Limieux (1998) [

θ t H = b H ( θ t H − θ t N ) + τ t H (9)

θ t N = b H ( θ t H − θ t N ) + τ t N (10)

where, b H and b N are projected yields coefficients with ( θ t H − θ t N ) being the ideal comparative advantage of pollinators. By the same token, the pollinators’ comparative advantage, π , is given by:

π t = ( θ t H − θ t N ) (11)

It follows that by substituting Equation (11) into (10), we have an expression for the case of when it is assumed pollinators have a digressive role in yields is given as:

θ t N = b H π t + τ t N (12)

Equally, yields projections case for when pollinators have an accretive role is given as:

θ t H = b H ( θ t H − θ t N ) + τ t H (13)

But θ t H − θ t N = τ t H

∴

θ t H = b H π t + τ t H (14)

By mathematically manipulating Equations (11) and (12), we have:

μ t H = b H π t + τ t H + ξ t H (15)

μ t N = b H π t + τ t N + ξ t N (16)

where, μ t H and ξ t H are standard error correlations in the model associated with the accretive role of pollinators. It is important to note that the three elements of ξ t H , ξ t N and X ′ j t s that are explained under Equation (5) are uncorrelated unlike θ t H and θ t N that are explained under Equations (9) and (10). By accounting for the standard errors, the unobserved components that are aggressive when discounted with the digressive ones, we have Equations (17) and (18) below where ξ t H and ξ t N are the respective transitory errors as:

y t H = β t H + X ′ j t γ j H + b t π t + τ t H + ξ t H (17)

y t N = β t N + X ′ j t γ j N + b t π t + τ t N + ξ t N (18)

By taking a generalized yield form, we have:

y t = h t y t H + y t N ( 1 − h t ) (19)

By combining Equations (17) and (18) through substitution, we have:

y t = β t N + h t ( β t H − β t N ) + X ′ j t γ N + X ′ t ( γ j H − γ j N ) h t + b N π t + ( b H − b N ) π t h t + a t + ε t (20)

y t = X ′ j t γ + π t ∫ j t × + β t + a t + ε t (21)

where, ∫ j t × , pollination, is determined exogenously. Also as expressed in Equation (21), quantity of yield is mainly determined by pollination and climate estimates.

Empirical ModelSpecifically, the model will adopt an econometric model that takes into account the climatic and non-climatic parameters. For purposes of completeness, let t be a time parameter for a given cereal yield in a given year. Further, the units of yields are quantified as tonnes. The mean annual temperature will be used as a climatic estimate in consistent with Southworth et al. (2000) [

Y t = β 0 + β 1 T m p t + β 2 P t n t + β 3 P o l l i n a t o r s t + ε t (22)

where Y t is the quantity of yields of a particular cereal crop (maize, rice, wheat and beans) produced over time t; β_{0} is the intercept; β_{1}, β_{2}, β_{3}, β_{4} are the coefficients; T m p t is the mean annual temperature; P t n t is the mean annual rainfall; P o l l i n a t o r s t are proxied by the number of beehives produced over time t; and, ε t is the error term.

Long-term historical data was used in this study. The data on the quantity of annual cereal crops (of maize and beans, rice and wheat) production was assembled from the Food and Agricultural Organization database over the period 1961 to 2017 while climatic data was assembled from the World Bank-Climate Change Knowledge Portal for the period 1961 to 2016. Using multivariate model, the data was analyzed to determine the influence of mean annual temperature, mean annual rainfall and pollinators on the aforementioned cereal yields.

The study employed a multivariate approach. The approach is ideal when multiple variables are established on the right side of the model equation, that way, linking with a number of variables (Hidalgo & Goodman, 2013) [

Variable | Unit | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|---|

InMaize | Metric tonnes | 14.61 | 0.34 | 13.75 | 15.16 |

InRice | Metric tonnes | 10.63 | 0.56 | 9.47 | 11.84 |

InWheat | Metric tonnes | 12.33 | 0.36 | 11.34 | 13.15 |

InBeans | Metric tonnes | 12.39 | 0.80 | 10.92 | 13.65 |

InPtn | Milliliters | 4.03 | 0.18 | 3.63 | 4.44 |

InTmp | Degrees Celsius | 3.21 | 0.02 | 3.16 | 3.25 |

InPollinators | Number | 14.15 | 0.61 | 13.12 | 15.30 |

Model One | Model Two | Model Three | Model Four | |
---|---|---|---|---|

Maize | Rice | Wheat | Beans | |

InPtn | 0.366*** (0.0279) | 0.137* (0.0590) | 0.292*** (0.0378) | 0.377*** (0.0589) |

InTmp | 9.776*** (0.277) | 19.00*** (0.586) | 5.064*** (0.376) | 19.52*** (0.585) |

InPollinators | 0.0407*** (0.0101) | −0.162*** (0.0213) | 0.248*** (0.0137) | 0.209*** (0.0213) |

Constant | −18.75*** (0.857) | −48.48*** (1.811) | −8.547*** (1.162) | −54.58*** (1.809) |

Observations | 1596 | 1596 | 1596 | 1596 |

R^{2} | 0.5197 | 0.4077 | 0.3525 | 0.5300 |

F-Stat^{ } | 574.2126 | 365.2018 | 288.8521 | 598.3496 |

P-value | 0.0000 | 0.0000 | 0.0000 | 0.0000 |

Standard errors in parentheses *p < 0.05, **p < 0.01, ***p < 0.001.

The results revealed that increasing amount of annual rainfall increases the yields of maize, rice, wheat and beans. It is further revealed that a unit increase in the amount of rainfall will result in a proportionate increase in cereal yields while a unit increase in temperature will result in varied increases in cereal yields. It is also revealed that a unit increase in the number of beehives (or increase in the number of bees―given that they are the world’s major pollinators) increases maize, beans and wheat yields production to a tune of about 25 percent but has resultant reducing effects for rice. All coefficients were statistically significant across the three levels of significance (1%, 5% and 10%). As indicated by the coefficients of R^{2}, over 50% of the data fit the regression models one and four while 40% and 35% of the data under model two and model three fit the regression model, respectively.

This study may not be the first but there are few studies that have linked climate change, pollinators and cereal yields in Kenya. An empirical understanding of this linkage at the time when the world has continued to experience temperature escalations is important in informing policy and enabling food security in the country, now and in the future.

In this study, data interpolation would not have provided intended estimates (Bloschl & Grayson, 2001) [

The findings revealed that a unit increase in the amount of rainfall resulted in a proportionate increase in cereal yields but a unit increase in temperature led to varied increases in cereal yields. This implies extensive use of extension services as observed by Di-Falco et al. (2011) [

Consistent with arguments posited by Wuebbles et al. (2014) [

The authors declare no conflicts of interest regarding the publication of this paper.

Nyangena, O., Senelwa, V.K. and Ngesa, R. (2020) Linking Climate Change, Pollinators and Cereal Yields in Kenya. Open Access Library Journal, 7: e6508. https://doi.org/10.4236/oalib.1106508