A Research Project Report submitted to the Department of Agriculture Economics and Management, Faculty of Agriculture of the University of Swaziland.

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!

order now


Machawe Mduduzi Mkhonta

In Partial Fulfillment of the Requirements for the Degree of Bachelor of Science in Agriculture Economics and Agricultural Business Management

Luyengo Campus, Swaziland

October 30, 2018

Author: Mkhonta Machawe Mduduzi

Signature: ………………………………… Date: ………………………….

Supervisor: Prof. J.O. Ajetomobi

Signature: ………………………………… Date: ………………………….
Approved for inclusion in the Library of the University of Swaziland, Luyengo Campus.

Head of Agricultural Economics and Management Department: Dr. S.G. Dlamini
Signature: ………………………………… Date: …………………………
Table of Contents
List of figures 5
List of tables 6
Abbreviations 7
1.0 Introduction 9
1.1.1 Greenhouse effect 11
1.1.2 Climate change 12
1.1.3 Climate Change on agriculture and food security, temperature and precipitation 13
1.1.4 Climate in Swaziland 13
1.1.5 Overview of Agriculture sector in Swaziland 14
1.2 Problem Statement 17
1.3 Justification of the study 18
1.4 Objectives of the study 19
1.4.1 Main Objective 19
1.4.2 Specific objectives 19
1.5 Hypothesis 19
1.6 Limitations of the study 20
2.0 Literature Review 21
2.1 Journal 1. 21
2.2 Journal 2: 23
2.3 Journal 3 25
2.4 Journal 4 27
2.5 Journal 5 28
2.6 Journal 6 29
2.7 Journal 7 30
2.8 Journal 8 30
2.9 Journal 9 31
2.10 Journal 10 32
3 Methodology 33
3.1 Study area 33
3.2 Sources of Data 34
3.3 Data collection Instrument. 34
3.4 Data Analysis 35
4.1 Descriptive statistics 37
4.2 Trend Analysis of Dairy yield 40
4.3 Trend Analysis of total annual rainfall 42
5.0 Summary, Conclusions and Recommendations 47
5.1 Purpose of the study 47
5.2 Summary 47
5.3 Recommendations 48
References 48

List of figures
Figure 1: Global mean surface-temperature change from 1880 to 2016, (NASA, GISS, 2017). 8
Figure 2: Agricultural commodities produced in Swaziland (USDA, 2016). 13
Figure 3: Map of Swaziland (CIA, 2016) 32
Figure 4: Butter; cow’s milk yield in metric tons from 1968 to 2017 (Wickham, 2009). 38
Figure 5: Total annual rainfall in millimeters from 1968 to 2017 (Wickham, 2009) 40
Figure 6: Seasonal mean temperatures in °C from 1968 to 2017 (Wickham, 2009). 42

List of tables
Table 1: Method of analysis as per objective 35
Table 2: Descriptive Statistics of annual yield, mean temperature and annual total rainfall (Diethelm, 2014) 38
Table 3: Regression analysis for seasonal mean temperatures on yield 43
Table 4: Regression analysis for variance in seasonal mean temperatures on yield 45

Abbreviations Definitions

ARMS Agricultural Resource Management Survey
CBS Central Bank of Swaziland
CCIA Climate Change Impact Assessment
CH4 Methane
CO2 Carbon Dioxide
E East
FAOSTAT Food Agricultural Organization Statistics
GDP Gross Domestic Product
GEF Global Environmental Facility
GISS Goddard Institute for Space Studies
IPCC Intergovernmental Panel on Climate Change
LAN Local Area Networks
LME London Metal Exchange
MPD Absolute Decline in Milk Production
N2O Nitrogen Dioxide
NASA National Aeronautics and Space Administration
NOAA National Oceanic and Atmospheric Administration
PRISM Parameter-elevation Regressions on Independent Slopes Model
S South
SNL Swazi Nation Land
SYMAP Synergraphic Mapping System
TDL Title Deed Land
THI Temperature-Humidity Index
UN United Nations
UNDP United Nations Development Programme
UNFCCC United Nations Framework Convention on Climate Change
USA United States of America
USDA United States Department of Agriculture
VLS Veterinary Livestock Services
WWAS World-Wide Airfield Summaries
WWD World Weather Disc

1.0 Introduction
This section consists of background, definition of global warming, causes and the likely effects of climate change on livestock and crop yields. It also includes an explanation and description of greenhouse effect and its possible implications in agriculture and food security. Overview of agriculture sector in Swaziland is also constituted in this chapter.
Background Information
Global warming, also referred to as climate change, is the observed century-scale rise in the average temperature of the earth’s climate system and its related effects. Multiple scientific researchers also prove that the climate system is warming. Many of the observed changes since the 1950s are unprecedented in the instrumental temperature record which extends back to the mid-19th century and in paleoclimate proxy records covering thousands of years (Wikipedia, 2017).

Figure 1: Global mean surface-temperature change from 1880 to 2016, (NASA, GISS, 2017).
The above graph shows global mean surface temperature change from 1880 to 2016, relative to the 1951–1980 mean. The black line is the global annual mean, and the red line is the five-year local regression line. The blue uncertainty bars show a 95% confidence interval, (NASA, GISS, 2017).
According to the National Geographic, (2015) global warming is the fast heating up of the planet leading to melting glaciers, rising sea levels, cloud forests dying due to human beings that have caused most of the past century’s warming by releasing heat trapping gases as they power up their modern lives. The resultant effect is called global warming because it is causing a set of changes to the earth’s climate, or long-term weather patterns, that varies from place to place (National Geographic, 2015).

Greenhouse effect

The “greenhouse effect” is the warming that happens when certain gases in earth’s atmosphere trap heat. These gases let in radiation from then sun but keep that radiation from escaping. First, sunlight shines onto the Earth’s surface, where it is absorbed and then radiated back into the atmosphere as heat. In the atmosphere, gases trap some of this heat, and the rest escapes into space. The more greenhouse gases are in the atmosphere; the more heat gets trapped (National Geographic, 2017). Rising fossil fuel burning and land use changes are emitting increasing quantities of greenhouse gases into the earth’s atmosphere. These greenhouse gases include carbon dioxide (CO2), methane (CH4) and nitrogen dioxide (N2O). A rise in these gases in the atmosphere has caused a rise in the amount of heat from the sun withheld in the earth’s atmosphere, heat that would normally be radiated back into space, (UNFCCC, 2007).
This increase in heat has led to the greenhouse effect, resulting to extreme weather conditions. The main characteristics of climate change are increased average global temperatures (global warming), changes in cloud cover and precipitation particularly over land, melting of ice caps and glaciers and reduced snow cover, increase in ocean temperatures and ocean acidity due to seawater absorbing heat and carbon dioxide from the atmosphere (UNFCCC, 2007).

Climate change

The intergovernmental Panel on Climate Change IPCC (2007) defines climate change as the change in the state of the climate that can be identified by changes in the mean and/or the variability of its properties, and that persists for an extended period. Climate change effects include among other things i.e. sea level rise, changes in the intensity, timing and spatial distribution of precipitation, changes in temperature and the frequency, intensity and duration of extreme climate events such as droughts, floods, and tropical storms. The IPCC (2007) asserts that climate change will have severe effects on the environment, especially water availability, agriculture and food security, human health, and biodiversity. Africa is already a continent under pressure from climate stresses and is highly vulnerable to the impacts of climate change. Many areas in Africa are recognized as having climates that are among the most variable in the world on seasonal and decadal time scales. Floods and droughts can occur in the same area within months of each other. These events can lead to famine and widespread disruption of socio-economic well-being. For example, estimates reported at the workshop indicate that one third of African people already live in drought-prone areas and 220 million people are exposed to drought each year. Many factors contribute and compound the impacts of current climate variability in Africa and will have negative effects on the continent’s ability to cope with climate change. These include poverty, illiteracy and lack of skills, weak institutions, limited infrastructure, lack of technology and information, low levels of primary education and health care, poor access to resources, low management capabilities and armed conflicts. The overexploitation of land resources including forests, increases in population, desertification and land degradation pose additional threats. (UNDP, 2006).
Climate Change on agriculture and food security, temperature and precipitation

Boko et al. (2007) and Christensen et al. (2007) short listed the impacts and vulnerabilities to climate change in Africa as severely compromised agricultural production due to loss of land, shorter growing seasons, more uncertainty about what and when to plant. Still on the same point, they stated that there is an increase to the number of people at hunger risk and worsening food insecurity. Yields from rain-fed crops could be halved by 2020 in some countries due to the same effects and lastly estimated net revenues from crops to fall by 90% by year 2100. On the other hand, temperatures are rising at 1.5 times throughout the African continent and in all seasons compared with global averages. They further stated that there decrease in annual rainfall in much of Mediterranean Africa and the Northern Sahara, with a greater likelihood of decreasing rainfall as the Mediterranean coast is approached, decrease in rainfall in southern Africa in much of the winter rainfall region and western margins and increase in annual mean rainfall in East Africa (Boko and Christensen et al., 2007).
Climate in Swaziland
Swaziland is classified to four climatic regions, the Highveld, Middleveld, Lowveld and Lubombo plateau. The seasons are the reverse of those in the Northern Hemisphere with December being mid-summer and June mid-winter. Rain falls mostly during the summer months, often in the form of thunderstorms. Winter is the dry season. Annual rainfall is highest on the Highveld in the West, between 1,000 and 2,000 mm depending on the year. The further East, the less rain, with the Lowveld recording 500 to 900 mm per annum. Variations in temperature are also related to the altitude of the different regions. The Highveld temperature is temperate and seldom uncomfortably hot, while the Lowveld may record temperatures around 40 ° C (104 °F) in summer (Wikipedia, 2017).
Overview of Agriculture sector in Swaziland
According to Swaziland Agricultural Economic Fact Sheet by the Foreign Agricultural Service United States Embassy in South Africa, Swaziland is a mountainous country surrounded mainly by South Africa and a small part by Mozambique and is estimated to be 17 364 square km. Agriculture sector in Swaziland is split between largely rain fed subsistence farming by smallholders and cash cropping on large private sectors. The smallholders constitute 70% of the population and occupy 75% of the crop land with low productivity at 11% of total agricultural output. The gross values of horticulture, livestock and field crop in 2015 were US$28 M, US$58 and US$166 M respectively of the total US$251M total gross value of Agricultural production at US$3.8 B total real GDP (USDA, 2016).

Figure 2: Agricultural commodities produced in Swaziland (USDA, 2016).

According to Thompson (2013) agriculture is the backbone of Swaziland’s economy and a major source of employment for rural households with more than 70% of the population relying on this sector for their income. The diverse agricultural activities that take place in the country include sugarcane production, citrus fruits, maize and other cereal crops, cotton, forestry and livestock production. In the year 2010, agriculture contributed an estimated 7.4% to the country’s GDP (Thompson, 2013). Swaziland’s agricultural sector is divided into two sub-sectors: formal and traditional agriculture. Informal agriculture is practiced under the Swazi Nation Land (SNL) tenure system where land is acquired in terms of traditional law and custom. Informal agriculture is mainly carried out for subsistence purposes although farmers are encouraged to engage in commercial farming. Traditional agriculture farmers produce mainly maize, sorghum, cotton, tobacco, goats, poultry, cattle, pigs, sheep and legumes such as beans and groundnuts. Assisted by the large estates and the Sugar Association, there has been an increase in the number of small cane growers who produce commercially on SNL. The formal agriculture sub-sector embraces the large sugar and citrus estates, forestry, beef and poultry production, dairy farming, fruit and vegetable production (Thompson, 2013).
Thompson (2013) also reported that the livestock subsector accounts for about 14% of agricultural output and 1% of total GDP. Swaziland has a relatively high unexploited potential of improving livestock production especially on Swazi Nation Land where 83% of the country’s livestock is reared. Livestock is a very important livelihood asset for the rural people in Swaziland. People derive their livelihood from livestock in the form of food, income, organic fertilizer, as a form of investment, power for ploughing their fields and for traditional ceremonies such as weddings.
According to a web page retrieved from export.gov (2016), Swaziland’s agricultural sector is the second largest contributor to the economy after the manufacturing sector. Commercial agriculture sector is dominated by sugar, canned fruit and beef production for export. Many Swazis practice subsistence farming, mainly maize cultivation. The country is historically a net importer of maize, and the quantity demanded depends significantly on unreliable rainfall. Besides subsistence production, the country meets much of its demand for agricultural products through imports from South Africa. In 2012, the annual imports and exports for milk and milk products was documented to be E214 Million worth of imported milk and milk products and E17.9 million worth of exported milk and milk products with a short fall of E197 million dairy production trade imbalance. (VLS, 2012). The livestock census data revealed that there were 3806 dairy cows and 474 farmers on Swazi Nation land (SNL), who are predominantly smallholder farmers. There were 1418 dairy cows and 55 farmers on title deed land (TDL). In contrast the 2011 census revealed that there were 2654 dairy cows and 462 farmers on SNL, with 2013 cows and 52 farmers on Title deed land (VLS ,2012).
The country’s demand for dairy products (milk and milk products) remained sustainably higher at 82.24 million liters in terms of liquid milk equivalence (LME) in 2016 compared to 81.67 million liters the previous year. Thus, the increase in local milk production only covered 17.7 per cent of the total demand. Though the increase in local milk production still fell significantly short of total demand (for dairy products), it did reflect good strides towards self-sufficiency. Dairy imports fell by about 5 per cent from 69.02 million liters (LME) in 2015 to 65.65 million liters (LME) in 2016 (CBS, 2017). In 2012/2013 maize cultivation increased from 76.0 metric tons in the previous year to 82.0 metric tons. Value-added activities in the sector include the processing and preserving of fruit and vegetables, the processing of vegetable and animal oils and fats, dairy products, grain mill products especially wheat which is wholly imported, prepared animal feeds, sugar refining, cocoa, chocolate, and sugar confectionery amongst other food products (CBS, 2017).
Problem Statement
According to Wikipedia (2017) milk is a white liquid produced by mammary glands and is the primary source of nutrition for infant mammals including humans. It contains many other nutrients including proteins and lactose. Human continue to consume milk from cows, goats and sheep beyond infancy as a food product as a food product eaten raw or processed to more other food products like cheese, milk powder, yoghurt etc. (Wikipedia, 2017).
According to the Swaziland Veterinary livestock service under the Ministry of Agriculture, (2012) dairy products in Swaziland are liquid milk and milk creams, milk powders and concentrated milk, yoghurt, milk containing products, butter, cheeses etc. These products are processed using milk from cows and goats milk which largely relies on pastures grasses for foraging in SNL and TDL. The rise in temperature will cause considerable variation in precipitation and the frequency of extreme climate events, droughts, floods, and forest fires are predicted to increase (IPCC, 2007). Mendelsohn et al; 2001 indicated that climate change implications are likely to be worse in developing countries especially in the agriculture sector. The dairy industry is facing a big challenge of decreased milk yields due to livestock malnutrition caused by poor grass pastures, heat stress, soil erosion, uncontrolled veld fires, land degradation, drought and late onset of rains. However, the issues reduced milk yield production from extreme weather conditions and other farm inputs as well as possibilities of the industry to adapt to this risk have not been scientifically investigated in the Kingdom’s case. This is important to guide the Ministry of Agriculture, Livestock and veterinary service department on how best to protect farmers in facing climate-related risks. Thus, this study is proposing to identify the potential impacts of extreme weather events on the productivity of milk yields in the Kingdom of Swaziland and its risk. (VLS, 2012).
1.3 Justification of the study
In agricultural dairy production, climate change has a great economical threat in almost all African countries including Swaziland and even worldwide. Demand for milk is increasing with the price on milk which is good for milk producers in Swaziland profit wise. However, due to ever increasing average climate temperatures and animal heat stress, there is fall in milk sufficiency which is caused by extreme weather effects on the animal, land degradation etc. Most dairy cows in Swaziland relies on poor pasture grasses for foraging. This has resulted in fall in milk yield (liters/cow) obtained per day. The importance of investigating climate change and variability as per its implications on milk yield to find alternative ways in which the industry can overcome these challenges.

1.4 Objectives of the study
1.4.1 Main Objective
To investigate the effects of climate change and variability of milk production in Swaziland
1.4.2 Specific objectives
Analyze the trend of milk production yield
Analyze the monthly trend of rainfall and temperature
To estimate the effect of climate change on milk production and overall yield

1.5 Hypothesis
The study is proposing to test the following hypothesis stated in null form:
N0 (i): There is no trend in the yield of milk production from 1968 to 2017.
N0 (ii): There is no monthly trend of rainfall and temperature from 1968 to 2017
N0 (iii): There is no relationship between climate change and variability to milk production and overall yield in Swaziland.
1.6 Limitations of the study
Regional data is not available and hard to obtain since time series data is observed and obtained over a longer period of time.
Statistical software’s to run regression models are hard and expensive to buy for a Uniswa Students since Stata v20 might be old and outdated.
Short time to investigate the climatic impact on agricultural yield is complex and needs extended periods of time while this study has to be concluded in 6 months’ time.
More studies have to be done to verify the validity of this study.
Lack of access to daily meteorological data on temperature and rainfall in Swaziland and daily temperature variation index of agricultural enterprise.
Limited access to fast internet connections since students rely on LAN which tends to be slow and used entirely by all students and staff for academic purposes.

2.0 Literature Review
This chapter constitutes reviews of literature of other researcher’s journals investigating the effects of climate change and variability on milk production abroad.
2.1 Journal 1.
Topic: The Potential Effects of Climate Change on Summer Season Dairy Cattle Milk Production and Reproduction.
Authors: Peggy L. Klinedinst, Kenneth G. Hubbard, G. Leroy Hahn, Donald A. Wilhite
Year: 1993

Main objective: investigate the potential effects of climate change on summer season dairy cattle milk production and reproduction.

Data collected: average monthly dry bulb and dew point temperatures for May through September, inclusive, were obtained from the WWD is a meteorological data base containing 17 data sets acquired from the archives of the National Climate Data Center, National Center for Atmospheric Research, and other sources, including foreign publications. The data for their study were taken from the data set on WWD. This data set contains climatological data for 5,717 airport locations around the world. The period of record for this data set is variable, but all data are pre-1974. The record length varies from 5 to 73 years.

Method of analysis: The researchers used an algorithm developed by Berry et al. (1964) and validated by Hahn (1969) which measures the possible direct effect of global warming on milk production. The research used well developed and tested biological response functions exist to quantify the effects of hot weather stress on animal productivity.

Berry et al. (1964) developed a quantitative relationship between declines in dairy cow milk production at various normal levels of production (NL, kg/cow/day) and the Temperature-Humidity Index (THI), 1 which incorporates the effects of air temperature and humidity for a range of 70 ? THI ? 84:

MPD = –1.075 – 1.736 NL + 0.02474 NL (THI)

MPD = absolute decline in milk production (kg/cow/day).
This model was developed for animals provided with shade but no other heat relief

Summary of Findings: milk production declines would be considerably greater in the United States than in Europe, and that such predicted declines for the scenarios would be generally higher than either “1 year in 10” probability-based declines or declines based on the abnormally hot summer of 1980. Several areas predicted to have maximum or high milk production decline correspond to areas of high dairy cattle concentration in the United States. This indicates the potential for a notable impact from global warming on summer season productivity for the overall United States dairy industry. The greatest declines in the United States are predicted to occur in the Southeast and the Southwest. These areas are already accustomed to relatively large summer season milk production declines, resulting from their normally warm summer season climate. Thus, the actual impacts of increased production declines may be greater in other areas, such as the northeastern United States, the midwestern United States, and Europe, which are not accustomed to large summer season declines and therefore have not adopted potential mitigation measures.

Conception rate declines were predicted to be greater in the United States than in Europe.
However, several stations in southern Europe may have considerable seasonal conception
rate declines. Conception rate decline may be of more concern from an economic
perspective in southern Europe than milk production decline.

2.2 Journal 2:
Topic: Impacts of Climate Change on Milk Production in the United States
Authors: Yoram Bauman, Eric P Salathé Jr, Guillaume S Mauger, and Tamilee D Nennich
Year: 2012
Main Objective: to estimate impacts of climate change on milk production in the United States

Data collected: Current and Future temperatures
The researchers used daily temperature data for 1950-1999 which were obtained from the 1/8° resolution gridded dataset developed by Maurer et al. (2002). The dataset, which includes daily minimum and maximum temperatures, was based on weather observations from the National Oceanic and Atmospheric Administration (NOAA) Cooperative Observer (Co-op) stations. As described by Maurer et al., the data are gridded using the synergraphic mapping system (SYMAP) of Shepard (1984), and adjusted to the mean grid cell elevation using an assumed lapse rate of -6.5 K/km.

The researchers’ future daily temperatures for the 2050s and 2080s were obtained by adding projected 140 temperature changes to the historic daily data. Their monthly-average temperature projections were obtained at 1/8° resolution (Maurer et al., 2007) from the Lawrence Livermore National Laboratory’s “Green Data Oasis”.

Method of Analysis:
In evaluating the effects of climate change, the researchers followed the approach in St-Pierre et al. (2003; hereafter SP2003) because of its tractability and extensive literature review. Based on data reported in their literature, they estimated the physiological effects of heat and humidity on Holstein dairy production by considering diurnal variations in a single parameter, the temperature humidity index (THI). The THI is calculated from temperature and relative humidity (0?RH?1) according to the formula from NOAA (1976).

THI = (1.8·Tair + 32) – (0.55 – 0.55·RH) (1.8·Tair – 26),

Where T air is the air temperature in degrees Celsius. The researchers also highlight THI increases linearly with air temperature if relative humidity is held constant, and that THI is simply air temperature in Fahrenheit if RH=100%.

Summary of results:
In this study, production losses were strongly influenced by geographic, seasonal, and diurnal variations in humidity and temperature. In most temperate regions, 21st century warming is projected to result in increased production losses as well as an increase in the number of days when cows experience heat stress. The researchers also found that cows within each region were generally raised in more temperate areas where losses related to heat stress were lower relative to the surrounding regions. Combining their estimated production loss per cow with county-level dairy populations, they obtain estimates of economic losses at the county level for the entire conterminous United States. Although localized impacts of heat stress can be quite severe and nationwide losses in excess of $2 billion per year should not be ignored, it is worth emphasizing that, relative to baseline production, climate change is only projected to reduce nationwide dairy production by 6.3% by the 2080s. Their results thus indicate that the impacts of climate change on nationwide milk production will be measurable, but modest.

2.3 Journal 3
Topic: The Potential Effects of Climate Change on the Productivity, Costs, and Returns of U.S. Dairy Production
Authors: Nigel Key and Stacy Sneeringer

Year: 2011
Main objective: investigate the potential effects of climate change on the productivity, costs, and returns of U.S. dairy production.

Data collected: investigated 2 data sets; dairy operational and climate data. Dairy operation data are drawn from the USDA’s collected in 2005. The ARMS survey targeted dairy operations in 24 States. Climate data from 1990 to 2009 was Parameter-elevation Regressions on Independent Slopes Model (PRISM), developed at Oregon State University.

Method of analysis:
Temperature humidity index (THI) (St-Pierre, Cobanov and Schnitkey, 2003) was a measure the researchers used to relate livestock productivity and climate. THI is calculated as:

THI=( dry bulb temperature ?)+(0.36 .dew point temperature?)+41.2

When animals are above a certain THI, productivity (in terms of weight gain, eggs laid, or milk produced) declines. Using their data on minimum and maximum temperatures (dry bulb temperatures) and the dew point, they generated a minimum and maximum THI for each month and location.3 Generally, livestock experience heat stress when the THI is above a specific threshold (for dairy this THI is 72).

Summary of results:
These preliminary results suggested that future climate changes that increase the THI load in dairy producing regions could affect the U.S. dairy sector – perhaps leading to higher dairy prices and/or altering the location of dairying. However, these graphical analyses are largely exploratory and provide only a descriptive relationship between climate and certain dairy production measures. As we noted in the conceptual framework section, valid comparisons of input levels, expenditures, and profits across climate regions require an adequate control for prices, technologies, and other factors that might be correlated with climate, which we did not do in this analysis. Future work will attempt to estimate an empirical relationship between climate and production that will allow us to predict how future climate change scenarios might affect dairy production.

2.4 Journal 4
Topic: The Impact of Climate Change on the Economics of Dairy Farming – a Review and Evaluation
Authors: Maria Martinsohn and Heiko Hansen
Year: 2012
Main objective: to review and evaluate the impact of climate change on the economics of dairy farming.
Data collected: Research papers on) deal with livestock farming – dairy.

Method of analysis: This research journal shed light on the present state of research in the field of dairy farming, one of the major sectors in agriculture, in a three-fold manner. First, potential climate change impacts in dairy farming are discussed qualitatively. Second, challenges and methodological approaches in economic CCIA are presented, with a closer look at the issue of climate data and farm-level adaptation. Third, an overview and assessment of available studies on economic CCIA in dairy farming along a set of evaluation criteria is provided.

Summary of results: The overview shows that existing studies are limited to only a few countries and climatic zones, which in fact do not belong to the areas presumably most affected by climate change. Also, from a methodological point of view of the journal, the use of simulation-based approaches predominates. This seems to be due to the fact that such approaches allow for a more focused assessment of climate change impacts, especially in view of expected conditions. Statistical/econometric methods require comprehensive and consistent data.
2.5 Journal 5
Topic: impacts of climate change induced changes in temperature on livestock production, i.e., dairy and beef cattle as well as pigs.

Authors: Mader et. al.,
Main objective: Assessing the impacts of climate change induced changes in temperature on livestock production, i.e., dairy and beef cattle as well as pigs

Data collected: dairy operational and climate data for two climate scenarios (temperature and rainfall data of past 30 years and future projections)

Method of analysis: Physiological production/response models for animals; focus on voluntary feed intake; climatic conditions from climate models

Summary of results: Dairy farming in the Great Plains of the USA will experience losses under climate change; milk production will decrease if no adaptation takes place.
2.6 Journal 6
Topic: Measuring impacts and adaptations to climate change: a structural Ricardian
model of African livestock management.

Authors: SEO and Mendelsohn

Year: 2008

Main objective: Analyzing how African livestock farmers decide under climate change.
Data collected:)–World Bank project to study climate change impacts on agriculture in Africa.
Method of analysis: Farmers’ decisions under specific climatic conditions are examined with a structural equation model using a sample of ten countries and more than 5 000 farms; based on this, impacts on net revenue are derived.

Summary of results: African farmers will adapt to climate change; while small farmers are able to switch species rather easily, changes come at significant cost for large farms; governments have to assist these adjustment processes.

2.7 Journal 7
Topic: Milk production decline during summer in Argentina: present situation and expected effects of global warming.

Authors: P.E. Leva, S.E. Valtorta and L.V. Fornasero

Year: 1996
Main objective: Measuring current and potential future milk yield loss due to heat stress

Data collected: Predicted versus Measured Production Differences Using Summer Air Conditioning for Lactating Dairy Cows.
Method of analysis: Using current and expected climatic conditions to calculate the THI and milk loss; reference situation is average milk yield under “normal” climatic conditions without heat stress.
Summary of results: Current milk yield losses due to heat stress are already significant in Argentina, especially in the northern part; expected climate change will aggravate the corresponding production losses.
2.8 Journal 8
Topic: Extent and economic effect of heat loads on dairy cattle production in Australia

Authors: Mayer, D.G., T.M. Davison, M.R. Mcgowan, B.A. Young, A.L. Matschoss, A.B. Hall, P.J. Goodwin, N.N. Jonsson and J.B
Year: 1999

Main objective: Assessing the impacts of heat loads on Holstein dairy cows and estimating milk yield loss and costs

Data collected: Data collected on Friesian and Ayrshire crossbred cows by Sudanese indigenous breeds of dairy cattle maintained at the University of Khartoum dairy farm.
Method of analysis: Use of long-term meteorological data to identify weather extremes over space and time; econometric estimation of production losses due to heat loads.

Summary of results: THI thresholds vary across the Australian regions, so cows might be adapted differently; production losses are greater for dairy herds with above-average milk yield; “good management” can mitigate these impacts

2.9 Journal 9

Topic: Feeding and housing of high performance cows and climate change.
Authors: Walter And Löpmeier,
Year: 2010
Main objective Analyzing dairy farming in various German regions under rising temperatures due to climate change

Data collected: Global Environmental Facility (GEF)–World Bank project
Method of analysis Physiological algorithms and THI-formula based on the literature; one scenario, but results of four climate models used.

Summary of results: The economic benefits of dairy farming (return from milk minus feed costs) will decline significantly in the long run; German regions will be affected differently; competitiveness of coastal regions will increase.

2.10 Journal 10

Topic: Climate change impacts on the livestock sector
Authors: Moran et al.,
Year: 2009
Main objective: Assessing climate change impacts on the British livestock industry
Data collected:
Method of analysis: Several bio-physiological models; assuming current prices; deduction of economic gains and losses.
Summary of results: Adaptation to climate change impacts such as increase in grass production, heat stress, exotic diseases is inevitable; the necessary adaptation is generally within the capacity of the British livestock industry

Study area
Swaziland is a landlocked country neighbored by Mozambique to its northeast direction and by South Africa to its north, west and south directions in the southern part of Africa. It is one of the smallest countries in Africa with a total area of estimated 17 364 square km and 1 467 152 total population lying at 26 30 S, 31 30 E geographical coordinates. Its climate and topography were diverse, ranging from a cool and mountainous Highveld to a hot and dry Lowveld. The natural vegetation is mainly Highveld grassland with very small patches of evergreen forest, and Lowveld tropical woodland, bush and savanna (Central Intelligence agency, 2017).

Figure 3: Map of Swaziland (CIA, 2016)
Sources of Data
To meet the objectives of the study, secondary data will be obtained from the) of the United Nations (UN) which will be time series data from 1968-2017 on monthly temperature and rainfall contained by World Bank Climate Data Portal.
Data collection Instrument.
The study will measure variables in the following manner. Rainfall will be measured in millimeters(mm), temperatures in degrees Celsius (°C), production in metric tons and area in hectares (ha).
Data Analysis
The annual trend of milk will be analyzed using line graphs and a Mann Kendal Trend Test will be used to test the positive and negative trends. The monthly rainfall and temperatures will use the same approach which is the use of line graphs and Mann Kendall trend test for positive and negative trend analysis. In Karmeshu’s research (2012), Mann Kendall is a statistical trend test widely used for the analysis of trend in climatological and in hydrologic time series. There were two advantages of using the test. First, it is non-parametric test and does not require the data to be normally distributed. Second, the test has low sensitivity to abrupt breaks due to inhomogeneous time series.

According to Karmeshu’s research (2012), Mann-Kendal is computed as follows:
S=?_(i=1)^(n-1)???_(j=i+1 )^n?, sign (T_j ?-T_i)
Sign (T_j-T_i )={?(1 if (T_j-T_i );0@0 if (T_j-T_i )=0@-1 if (T_j-T_i ) i, respectively. If n ; 10, the value of |S| is compared directly to the theoretical distribution of S derived by Mann-Kendall. The two-tailed test is used. At certain probability level H0 is rejected in favor of H1 if the absolute value of S equals or exceeds a specified value S?/2, where S?/2 is the smallest S which has the probability less than ?/2 to appear in case of no trend. A positive (negative) value of S indicates an upward (downward) trend (Karmeshu, 2012).
According to Ching-Cheng Chang (2002)’s research, to analyze the effect of climate change on milk production and overall yield, a multiple regression analysis approach is used which uses Yield response approach as cited in Kaiser et al., 1993; Easterling et al., 1993). Crop yield response model are typical estimated from field data using the measurement of non-climate related variables and climate to find the physical effect of climate change on the yield. The general form of this model is given by yield = f (climate, temperature, livestock units, production), (Ching-Cheng Chang, 2002).

Objectives Method of analysis
Examine the trend of Dairy production in Swaziland.
The annual trend of the yield will be analysed using line graphs and Mann Kendall trend test will be used to test for the existence positive and negative trends.

Analyse trend of rainfall and temperatures in Swaziland.
Rainfall and temperature will be analysed using the Mann Kendal trend test.

To estimate the effect of climate change and variability on Dairy production.
` The yield will be measured using a multiple regression to the analyse re determinants of yields.

Table 1: Method of analysis as per objective

4.1 Descriptive statistics

The descriptive statistics of the climate data obtained from the Food and Agricultural Organization (FAOSTAT) of the United Nations (UN) which was time series data from 1968-2017 (50 years) on monthly temperature and rainfall used in this study; represented in table1.
Monthly temperatures were grouped into seasons. With the help of Microsoft Excel, mean temperatures were calculated to represent temperatures of four different seasons which were spring, summer, winter and autumn. Summer months were December, January and February. Autumn months were March, April and May. Winter months were June, July and August. Spring months were September, October and November. Seasonal mean temperatures were represented by SpringT, SummerT, AutumnT and WinterT. The minimum annual temperatures that Swaziland recorded since the past 50 years were 16.3 ° C, 21.5 ° C, 18.7 ° C and 18.6 ° C for SpringT, SummerT, AutumnT and WinterT respectively. The maximum annual temperatures that Swaziland recorded since the past 50 years were 22.3°C, 24.3 ° C, 22.3 ° C and 22.329 ° C for SpringT, SummerT, AutumnT and WinterT respectively.
The maximum dairy yield was 218 metric tons recorded in the early 1990’s and the minimum dairy yield 92 metric tones recorded in 1968. The mean in the distribution of temperatures for the past 50 years for SpringT, SummerT, AutumnT and WinterT is 20.4°C, 23.1°C, 20.5°C and 20.5°C. The data also reflects that the mean rainfall is 830.8 millimeters from 1968 to 2017. The total rainfall recorded in Swaziland has minimum of 518.2 millimeters, a maximum of 1479 millimeter.

Descriptive Statistics of annual yield, mean temperature and annual total rain

Year Yield SpringT SummerT AutumnT WinterT TotalR

No. of observations 50 50 50 50 50 50 50

Minimum 1,968 92 16.254 21.500 18.660 18.660 518.178
Maximum 2,017 218 22.340 24.321 22.329 22.329 1,479.034
1. Quartile 1,980.250 153 19.958 22.545 19.738 19.738 733.621
3. Quartile 2,004.750 192.994 20.913 23.693 21.020 21.020 919.673
Mean 1,992.500 168.564 20.406 23.191 20.479 20.479 830.757
Median 1,992.500 169 20.566 23.417 20.657 20.657 854.833
SE Mean 2.062 4.094 0.140 0.099 0.123 0.123 23.033
LCL Mean 1,988.357 160.337 20.124 22.992 20.231 20.231 784.470
UCL Mean 1,996.643 176.790 20.688 23.391 20.726 20.726 877.044
Variance 212.500 837.918 0.984 0.494 0.759 0.759 26,526.480
Stdev 14.577 28.947 0.992 0.703 0.871 0.871 162.870
Skewness 0 -0.432 -1.518 -0.408 -0.075 -0.075 0.911
Kurtosis -1.272 -0.259 4.838 -0.836 -0.684 -0.684 3.199

Table 2: Descriptive Statistics of annual yield, mean temperature and annual total rainfall (Diethelm, 2014)
4.2 Trend Analysis of Dairy yield

Figure 4: Butter; cow’s milk yield in metric tons from 1968 to 2017 (Wickham, 2009).

Cow milk and butter yield data obtained from Food and Agricultural Organization (FAOSTAT) was graphically presented using ggplot2 package (Wickham, 2009). Annual dairy yield (cow’s milk and butter in metric tons) produced in Swaziland for the past 50 years (1968 to 2017) were shown in figure 4. Different patterns of irregularities in growth rate and decline are shown indicating positive overall growth on the yield of dairy. The equation of the line of best indicates a yield growth rate of 1.08 metric tones per year
The yield trend of dairy as shown in figure 4 is upward sloping and consistent between 1968 to 1980 and 1987 to 1994, respectively. In 1980 to 1987, there was a decline in the growth rate of the yield. Eventually, yield growth was restored from 1987 to 1994. Despite the upward sloping line of best of best fit, there was a remarkable decline in milk production of 239 metric tons to 140 metric tons to from 1994 to 1999. According to Reliefweb (2015), this might be due to negative influence of a number of factors, both natural and artificial. Such factors may include droughts which have a negative effect on reliable water resources, livestock diseases and lack resistant hybrid breeds of dairy cattle (Reliefweb, 2015).
From 1999 to 2005 milk growth rate sharply increased from the 140 metric tones to 220 metric tons. This could be influenced positively by new amended policies that enable dairy farmers subsidies, improvement in feed, Swazi Dairy Board intervention in providing farmers with hay bales for to ensure that feed is available throughout the winter season, provision of supplementary feed and improve in pasture management practices. From 2006 to date milk yields have been steadily increasing. Extension services and dairy development projects by the Swaziland Dairy Board could have played a significant role in increasing the yields despite a slight decline on the overall annual rainfall in mm as evidence of effects of global warming (SDB,2015).

Figure 5: Total annual rainfall in millimeters from 1968 to 2017 (Wickham, 2009)

4.3 Trend Analysis of total annual rainfall
Figure 5 indicates different patterns of annual total rainfall in millimeters over the years as evidence of the negative effects of global warming which causes changes in climate data. There is an increase in global temperatures and decline on rainfall. The trend indicated by total rain recorded from 1968 to 2017 in figure 5 shows a slight downward slope. The line of best fit equation shows a decline of 0.977 millimeters per year. However, the pattern is said be irregular over the past 5 decades. The significant peaks and nadirs in the graphs indicates floods and droughts which are strong evidence of effects of global warming.
For instance, in 2000, heavy rains and flooding in Swaziland destroyed at least 10 percent of the country's cotton crop resulting a loss of revenue earnings estimated at over US $9 million, according to the Ministry of Agriculture. The Kingdom also realized reduced yields of the national staple, maize, by 37 percent. According to the country's National Disaster Relief Unit, Swaziland had to import 66,000 metric tons of maize (Reliefweb, 2015).

According to (Reliefweb, 2015).), the country was also assisted by donors to make up the remainder of the country's annual 138,000 metric tons requirements then. Also, a report confirmed that bean and sweet potato crops were virtually wiped out. Farmers could not plant new crops because flooded fields along the swollen Black Mbuluzi river and Komati river which flow into southern Mozambique were flooded (Reliefweb, 2015).
On the other hand, the nadir on 2015 was recorded total annual rain of 550mm which was due to the El Nino drought that hit most southern parts of Africa. On 18 February 2016 the Government of Swaziland declared a national state of emergency due to the drought, as El Nino impacts become more apparent. Maize production fell by 31 per cent in 2015 placing at least 300,000 people – a third of the population – in dire calamity need of assistance, specifically with for food and water. (Reliefweb, 2015).

Figure 6: Seasonal mean temperatures in °C from 1968 to 2017 (Wickham, 2009).
As evidence of global warming, annual mean temperatures were also showing an alarming increasing rat6e of0.0215 ° C. It also predicted that by 2040, Africa average mean temperatures would increase by 2°C from todays average mean temperatures.

Dependent variable:

Variables log(yield)

log(spr) -0.099

log(sumr) -0.115

log(autn) -0.224**

log(wint) 0.020

log(totalrain) 0.063

Constant 6.437***

Observations 50
R2 0.187
Adjusted R2 0.094
Residual Std. Error 0.177 (df = 44)
F Statistic 2.018* (df = 5; 44)

Note: *p