POTENTIAL USE OF DRY SPELL INFORMATION FOR FARMER IN-SEASON
DECISIONS
Zuma-Netshiukhwi, GNC 12 and Walker, S 2
1 ARC-ISCW, Private Bag X01, Glen, 9360, South Africa;
2 University of the Free State, PO Box 339, Bloemfontein, 9301, South Africa;
E-mail: gugun@arc.agric.za
In the semi-arid areas farmers use the onset of rain to determine planting dates for summer crops. A detailed knowledge of the rainfall pattern and distribution is the first step to proper planning for agricultural activities. Agro-climatic resources, most importantly rainfall have a significant impact on crop growth and development. However, the onset of rainfall in rainy season and its distribution are erratic. Analyzing the characteristics of dry spells is helpful to manage water supply effectively and efficiently. Glen Agricultural College weather station in the south-western Free State was selected for this study. Daily rainfall was investigated to determine the probability of maximum dry spell lengths of 7, 10 and 15 days from the rainfall datasets. This exercise is done to determine occurrences of drought conditions through the crop growing season. The study used the Markov first order chain in INSTAT PLUS statistical package to determine the probability of occurrence of dry spells at a given threshold of daily rainfall. Rainfall variability is a major contributing factor in crop yield reduction. The study also investigated the relationship between the rainfall and Southern
Oscillation Index on a seasonal basis. During El Niño years prolonged dry spells occurred often but in La Nina years there was only about a 20 % probability of occurrence. Most farmers in the southwestern Free State produce under rain-fed conditions but some have no access to climate/weather advisories. Results from this study were presented to resource poor and commercial farmers to help them manage water supply during prolonged dry spells. Water stress of various intensities at different growth stages of the crop affects and limits the growth rate and plant development, resulting reduced crop yield. However, development of adaptable and area-specific improvement strategies and point specific advisories are emphasized to minimize crop failure and improve crop production. Farmers can benefit by understanding and being able to use such information, and therefore to improve production, since agricultural enterprises are highly influenced by the amount and distribution of rainfall received.
Key words: Decision making, dry spells, rainfall, probabilities

1.
Introduction
Sustainable methods in optimizing crop water productivity have increasingly gained significance for research in semi-arid regions (Debaeke & Aboudrare, 2004). Agricultural research has concentrated on maximizing the final production for better financial returns to the farmers. However, water and land have been limiting factors in crop and animal production systems, due to a rapidly growing world population (Howell, 2001). With this background, rain-fed crop production has been a mainstay to resource-poor farmers and farms that have no access to supplementary water for irrigation (Fereres & Soriano, 2007). Increasing crop production in semi-arid areas remains a challenging task due to rainfall that is low and erratic (Heng et al., 2007), although it is the primary source of water. The success or failure of crops under rain-fed conditions is determined by the rainfall patterns and amount received. Therefore, rainfall amounts and seasonality are essential to an in-depth understanding of the biotic and abiotic environment. Rainfall trends are crucial to optimizing the spatial distribution and adaptability of different agricultural enterprises (Monadjem &
Perrin, 2003).
Research on the climate of South Africa has received much effort in the past two decades due to a notable reoccurrence of droughts causing reduction in crop yields. (Makarau & Jury, 1997).
Understanding dry spell occurrences is crucial because in the Free State Province economics is still driven by agriculture to a certain extent. The Free State climate is semi-arid, characterized by an annual rainfall of less than 700 mm accompanied by annual potential evapotranspiration of above
1800 mm within the steppe ecological zones. In semi-arid areas, rainfall is known to be irregular in its occurrence and the rainfall distribution is uneven throughout the season (Fischer & Turner,
1978).
Water supply from rainfall is in high demand throughout the world for agricultural and other uses.
Therefore, rainfall pattern analysis, particularly the dry spell characteristics, is important to agricultural enterprises to be able to manage the water supply effectively during lengthy dry days.
According to Sivakumar et al . (1993) a dry spell is defined as a continuous period of dry days during the rainfall season lasting for 10 days or more. Prolonged dry spells can have a noticeable negative effect on crop growth rate and development (Barron et al.
, 2003). Prolonged dry spells can cause noticeable limitations of crop growth and the effects differ at different plant stages. For example, exposure to severe water stress during the vegetative phase would reduce total leaf area and during the flowering stage it results in yield reduction. Dry spells of more than 30 days during
2

mid-season without any means to mitigate the situation cause the crop to reach its permanent wilting point and in due course results in crop failure, which causes food insecurity (Rockstrőm, 2000). Dry spell occurrences vary from season to season, year to year and decade to decade in lengths and numbers within the plant growing season (Chibulu, 2007). Therefore, daily rainfall and information about dry spell length is imperative to influence the decisions made by farmers across a variety of agricultural enterprises. However, the relationship between rainfall and the Southern Oscillation
Index should also be addressed.
This study explores the probability of receiving dry spells at Glen, in central Free State Province together with the relationship between monthly rainfall and the Southern Oscillation Index. An increase in the trade winds and warming sea surface temperature in the eastern and Pacific Ocean is called El Niňo (Jin. et al , 2004) whilst La Niňa occurs when sea surface temperature in central
Pacific Ocean is below normal. The El Niňo and La Niňa situations characterize a seesaw trend in atmospheric pressure at Tahita and Darwin known as the southern oscillation (SO). This oscillation is defined by an index, the Southern Oscillation Index (SOI) and is now used to identify the occurrence of El Niňo or La Niňa events (Jin, et al , 2004; Japanese Group for Climate Impact and
Application, 1999). These oscillations are collectively known as El Niňo/ Southern Oscillation or
ENSO (Stone & Mienke, 2006). The monthly SOIs have been associated with amounts of rainfall probability distributions in the southern hemisphere (Stone & Auliciems, 2006). Advances have been made in understanding the SOI-based seasonal climate forecasts and they have been used to improve agricultural management and planning in Southern Africa so as to minimize adverse crop failure (Phillips, 2001; Chikoore & Unganai, 2001).
Lack of rain and dry weather conditions adversely affect farmers. During the rainy season prolonged dry spells affects crop growth and development due to high temperatures that increases the rate of evapotranspiration. As a result, soil water deficit conditions prevent suitable plant growth and development. There are some opportunities for farmers to adapt and apply appropriate mitigation techniques to inhibit the effects of dry spells and lessen the soil water depletion (Fischer & Savenije,
2009). Therefore, it is critical to understand rainfall patterns, trends, distribution and dry spell occurrences which could provide key factors to better planning and management of a dryland crop production system. This information is of great importance in adapting suitable mitigation techniques for rain-water conservation and using the probability of experiencing dry spells of various lengths at critical growing stages to change some other management practices.
3

2. Materials and Methods
2.1 Study area
This study was conducted in the south-western Free State Province, South Africa, using the Glen weather station situated at -28º92"08' longitude and 26º35"94' latitude and at 1344m altitude. It is commonly known as Glen Agricultural College weather station and is located within the
Modder/Riet catchment. It has observations since 1922 to date. It has recorded daily rainfall and other agrometeorological parameters with the most notable drought year in the past decade being
1999. This station was also selected due to the nature of agricultural activities in this area.
Consequently, analyzing daily rainfall data representing this area and relating it to SOI would give a better understanding of the relationship between SOI and Glen station rainfall.
2.2 Data analysis
The long-term daily rainfall dataset of 49 years from1950-1999 for Glen Agricultural College weather station (from the ARC-ISCW Agro-Climate databank) was analyzed for dry spells during the summer rainfall season. The season starts from September and goes until March which is the plant growing and precipitation period. The INSTAT PLUS statistical package was used to calculate the occurrence of dry spells of more than 7, 10 and 15 days using a 0.85 mm per day threshold as described by Stern et al.
(1982). The (SOI) values for September until March, from 1950 to 2008 were downloaded from the International Research Institute for Climate and Society, website
( http://iridl.ideo.columbia.edu/index.html
)
3. Results and Discussion
3.1 Probability of dry spells
The prediction of a prolonged dry spell allows the farmer to prepare for supplementary irrigation and to make decisions on which agricultural activities to engage in, such as when to weed or apply mulch where possible, and mainly, when to plant. The best time to plant can be determined by using the low probability of getting prolonged dry spells. That is, when the pre-planting soil water content would be optimal. Figure 1 shows the probability of receiving 7-day, 10-day and 15-day dry spells during the September to March summer season for Glen station.
4

50
45
40
35
30
25
20
15
10
5
0
Sep Oct Nov Dec Jan
Feb Mar
Figure 1. Probability of dry spells of varying length (namely 7, 10 and 15 days) during the summer rainfall season for Glen weather station.
At the beginning of the season during September and October there is a 30-40% probability of experiencing a 7-day dry spell and this remains about the same throughout the season. Considering the probability of a 15-day dry spell, it is relatively higher at the beginning of the season with a 25-
35% chance but then decreases later in the season to a 11-12% chance during February and March.
This is in line with a higher rainfall amount received during those months. The probability of a 10day dry spell ranges from 19-33% with a reduced chance later in the season. Consequently, the decreasing chances of 10-15 days are critical and the rainfall will be beneficial for crop growth and development. Optimal planting must take place when prolonged dry spells (10-15 days and above) have declined, for example in December the 15-day dry spell probability is less than 15% otherwise germination and emergence as well as initial crop vegetative development may suffer due to severe water stress. The critical dry spells occurred during October and February where in the 15-day probability reached 40%.
Agriculture is highly vulnerable to growing season to growing season and year to year climate variability. This variability can be devastating to rain-fed agriculture and grazing due to uncertainty of the next growing season. As a result, famers are unable to make proper decisions and prepare accordingly for the next season. Understanding and availability of agrometeorological information
5

three months before the season may minimize the severe impact of climate. Dry spell probability information can be of benefit to help farmers to plan for their in-season and off-season agricultural activities.
Dry spell occurrence information for a specified location is critical in selecting crop types and suitable cultivars that are drought resistant. Prior knowledge of dry spell probabilities assists in proper planning for application of mulch, adapting in-field or simple water conservation tillage methods and supplementary irrigation at appropriate times. For operational planning they can be used to select the best for planting, hoeing, weeding, spraying and application of fertilizers. Dry spell occurrence information is therefore essential to farmers for tactical and operational planning.
3.2 Rainfall and SOI relationships
3.2.1 Seasonal rainfall data
The rainfall season at Glen station starts in September and goes until March (Ehlers, 1988). Longterm monthly rainfall data for September till March is presented in Figure 2. Table 1 shows the historical statistical summary for rainfall at Glen. The rainfall distribution and amounts are highly erratic in time and space (Figure 2). Below-normal rainfall in received during September, October and December. Hence, above-normal rainfall is received during November, January, February and
March. The seasonal average monthly rainfall is about 65 mm for Glen weather station (Figure 2).
The peak of mean rainfall is experienced in February with 89.02 mm, followed by January which receives a mean of 84.8 mm rainfall (Table 1).
6

320
300
280
260
240
220
200
180
160
140
120
100
80
60
40
20
0
1951 1956 1961 1966 1971 1976
Years
1981 1986 1991 1996
Figure 2. Long-term monthly rainfall for September till March for Glen station.
Table 1. Statistical summary of historical monthly rainfall for Glen station
Months
Mean
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
86 91 80 49 22 10 8 12 22 46 66 65
Median 73 72 74 39 15 4 1 4 10 38 59 55
Standard
Deviation
Highest recorded
Lowest recorded
53
164 229 209 211 123 52 51 117 189 216 190 172
9
73
5
47
3
39
0
24
0
14
0
13
0
21
0
33
0
40
0
42
1
45
0
7

Rainfall data analysis presented in Figure 2 and Table 1 is most important for in-season decision making. This information can influence farmers, researchers and policy-makers for better planning and management to farmers. Therefore, the relationship between rainfall and SOI should also be considered.
3.2.2 Monthly rainfall and SOI correlation
The SOI indicates the atmospheric pressure oscillation across the Pacific Ocean. It is expressed as the mean sea level pressure between Tahiti and Darwin (Jin. et al., 2004; McKeon & White. 1992).
Positive SOI is an indication of strong trade winds blowing across the warm Pacific Ocean and accumulating sufficient moisture to produce rain. Negative SOI is an indication that the trade winds across the Pacific Ocean weakened and the rainfall in the south-eastern Pacific islands becomes below normal(Jin. et al., 2004). A SOI value of less than -1 represent El Niňo conditions and a value of more than 1 represents the occurrence of La Niňa conditions (Kawamura, et al , 2002).
Rainfall and SOI relationship for September till March shows that when the SOI values were positive, then normal to above-normal rainfall conditions were recorded at Glen station. The events that occurred from 1950-2008 are presented in Figure 3. The events in 1955-1956, 1973-1976 and
1988-1989 presents a condition when, the SOI values ranged from +2 to +4.5 as La Nina events occurred during these years. The rainfall was above normal and floods occurred. For instance, in
1988 floods were recorded in South Africa nationwide.
The negative SOI values between 0 and -1 experienced during 1951-1954, 1965-1966, 1982-1983,
1986-1987, 1991-1995, 1997-1998 and 2005 (Fig. 3) indicate El Nino years. During these years normal to below-normal rainfall occurred. Extreme El Nino events were experienced in 1958, 1961,
1978, 1992, 1998, and 2005 (Figure 3). During these years below-normal rainfall was recorded and drought conditions were experienced.
The SOI values recorded from September till March were -2 to -6.7. The farmers around Glen and across the country experienced crop failure and this condition resulted in severely reduced final crop yields. Calculating the linkage between rainfall and SOI is very crucial as it can be used as a predictive tool for rainfall probabilities. However, rainfall is not only influenced by the SOI but also sea surface temperature, trade winds and other factors.
8

0
SOI
-2
-4
-6
-8
-10
6
4
2
1951 1955 1959 1963 1967 1971 1976 1980 1984 1988 1992 1996
Years
Figure 3. Time series of seasonal SOI from September till March
(( http://iridl.ideo.columbia.edu/index.html
)
Mean seasonal rainfall versus mean SOI for the September to March rainy season is presented in
Figure 4. This graph expresses the correlation between long-term means SOI versus mean rainfall
(dots) using a linear regression (line). A significant correlation (R 2 = 0.44) exists between mean seasonal rainfall and mean SOI (Figure 5), considering that precipitation is not only related to SOI but is also influenced by other factors. The SOI is positively correlated with seasonal rainfall forecast for Glen weather station. Therefore, SOI at Glen station should be given weight for proper and advanced decision making for agricultural activities.
9

-0.5
y = -134.91x + 22.637
R
2
= 0.4373
-0.4
-0.3
-0.2
SOI-Mean Rain
SOI-Mean Rain
-0.1
100
90
80
70
60
50
40
30
20
10
0
0
Figure 5. Correlation of long term means for rainfall and SOI for the September to March season.
Therefore, the dry spell probability information, rainfall analysis, and SOI correlation with rainfall could all play a crucial role in decision making. Provision of agrometeorological advisories could be beneficial as these have the potential to improve farming strategies suitable for the next season
(Stone & Meinke, 2006). For example seasonal climate forecast produced by the South African
Weather Service properly disseminated and explained explicitly to farmers and others in the agricultural sector, is the key to proper decision making.
4. Conclusions
Daily rainfall data can be used to predict dry spell probabilities for better strategic agricultural planning and management in semi-arid areas. Prolonged dry spells can have detrimental effects on crop rate growth, development and yield. Farmers can benefit from understanding and using this information, as agricultural enterprises are highly influenced and affected by the amount and inconsistency of rainfall received. High variability in rainfall distribution and amounts together with high evaporative demand leads to severe limitations for crop growth and yield in semi-arid areas.
Probability of experiencing dry spells of various durations at critical crop growth stages is of importance in preparing farmers to adopt mitigation strategies.
10

The significant correlation of SOI and rainfall for Glen station allows the monthly SOI values to be useful for seasonal rainfall forecasts. The information is beneficial for improving and making informed decisions for operational agricultural activities. A positive SOI corresponds with aboveaverage rainfall while a negative SOI corresponds with below-average rainfall amounts. Therefore,
SOI has direct application to agricultural decision making as it can be used to predict precipitation using different lag times.
5. Acknowledgements
This work was supported by funds from the Water Research Commission for the Project
“Applications of Rainfall Forecasts for Agriculturally Related Decision Making in Selected
Catchments” as well as the Agricultural Research Council and the University of the Free State.
References
Barron, J, Rockstrőm, J, Gichuki, F & Hatibu, N, 2003. Dry spell analysis and maize yields for two semi-arid locations in east Africa. Agricultural and Forest Meteorology 117: 23-37.
Chibulu, B. 2007. Effect of rainfall variability on crop yield under semi-arid conditions at subcatchment level, M.Sc. Thesis University of Zimbabwe.
Chikoore, H. & Unganai, L., 2001. Integrating climate forecast information in agricultural management: A perspective from Southern Africa. Global Environmental Research Community
Meeting, Rio de Janeiro.
Debaeke, P, & Aboudrare, A, 2004. Adaptation of crop management to water-limited environments,
European Journal of Agronomy 21: 433-446.
Ehlers, J.F. 1988. Ekologise beplanning van gewasse. Department van Landbou-tegniese Dienste,
Transvaalstreek.
Fereres, E, & Soriano, M.A., 2007. Deficit irrigation for reducing agricultural water use. Journal
Experimental Botany 58: 147-159.
Fischer, B.M.C. & Savenije, H.H.G., 2009. A spatial and temporal rainfall analysis of the Pangani basin and Makanya catchment, Tanzania. Geographycal Research Abstracts. 11:79-86.
11

Fischer, FR & Turner, NC., 1978. Plant productivity in the arid and semi-arid zones. Annual Review of Plant Physiology. 29: 277-317.
Heng, LK., Asseng, S., Mejahed, K., & Rusan, M., 2007. Optimising wheat productivity in two rain-fed environments of the West Asia-North Africa region using a simulation model, European
Journal of Agronomy.
29: 121-129.
Howell, T., 2001. Enhancing water use efficiency in irrigated agriculture. Agronomy Journal 93:
281-289.
Japanese Group for Climate Impact and Application, 1999. El Niňo and Global Environment.
Seizando, Japan in Japanese.
Jin, YH, Kawamura, A, Jinno, K & Berndtsons, R, 2004. Quantitative relationship between SOI and observed precipitation in sourthern Korea and Japan by nonparametric approaches. Journal of
Hydrology volume 301: 55-65.
Kawamura, A., Eguchi, S. & Jinno, K., 2002. Long-term fluctuation characteristics of Southern
Oscillation. Annual Journal of Hydraulic Engineering, JSCE 46: 103-108.
Makarau, A., & Jury, M.R., 1997. Seasonal cycle of convective spells over southern Africa during austral summer. International Journal of Climatology . 17: 1317-1332.
McKeon, G.M. & White, D.H. 1992. El Niňo and better land management. Search, 23 (6): 197-200.
Monadjem, A. & Perrin, M. 2003. Population fluctuations and community structure of small mammals in a Swaziland grassland over a three year period. African Zoology 38: 127-137.
Phillips, J.G., 2001. Current and potential use of climate forecasts for resource poor farmers in
Zimbabwe. Impact of El Nino and Climate Variability on Agriculture. In American Society of
Agronomy, Crop Science Society of America, and Soil Science Society of America, Madison, USA.
ASA Special Publication, 63.
Rockstrőm, J. 200. Water resource management in smallholder farms in eastern and southern Africa:
An overview. Physics, Chemistry and Earth (B) 25 (3): 257-83. Elsevier Ltd.
Sivakumar,M.V.K., Maidoukia, A & Stern, R.D., 1993. Agroclimatology of West Africa: Niger.
Information Bulletin no 5. Patancheru, India: International Crops Research Institute for the Semi-
Arid Topics.
Stern, R.D., Dennett, M.D. & Dale I.C, 1982. Analyzing rainfall measurements to give agronomically useful results. II. Modelling approach. Exp. Agric.
18: 237-253.
Stone, R. & Auliciems, A., 2006. SOI phase relationships with rainfall in eastern Australia.
International Journal of Climatology . 12(6): 625-636.
Stone, R.C. & Meinke, H., 2006. Weather, Climate and Farmers: An overview. Applied
Meteorology.
7-20.
12

13