Ground water withdrawal turned out to be a serious problem in western United States, parts of India, China and Mexico. Salinization and waterlogging of soil are other serious problems associated with irrigation. In this new scenario, the management of water resources should fall in the core of all adaptation strategies. Where there is no threat of water scarcity, demand would not increase.
However, if more and more land is brought under irrigation, water scarcity will certainly occur. Farmers have begun falling back on traditional ways, such as reusing water from washing utensils to irrigate kitchen gardens.
Many communities are also reviving traditional water harvesting, a method of storing rainwater. In some cases, farmers changed their crops to suit the prevailing conditions. Although there is no one-stop solution, traditional methods of dealing with low rainfall propel us to build innovative, scientific methods to deal with such changing rainfall patterns.
Check our Live demo: www. SourceTrace's software solutions have been deployed across 37 countries and 4 continents already. We are on a mission to make agriculture and food systems more sustainable. Get in touch and we will extend our expertise and commitment to you. Read full article. In recent times, changes in rainfall distribution, interseasonal fluctuations, and erratic rainfall patterns have been reported to have resulted in reductions in crop production which has become a major issue to farmers and policy-makers as threats to food security as reported elsewhere [ 11 , 13 , 24 — 26 ].
This is especially so for the major crops produced in Ghana, namely, cassava, cocoyam, plantain, and tomatoes [ 39 ]. Also, more recent statistical evidences show that interannual variability has increased within the major and minor rainy seasons, while the minor rainy seasons have become much drier and shorter GMet even though the report provides very little details on specific localities where these trends were observed including major crop producing areas which includes the WAA of the semideciduous areas of Ghana.
WAA constitutes a unit in developing a national map for agricultural performance in climate variability or change and for decision-making in resolving challenges within the agricultural sector of the semideciduous areas which have rarely been studied relative to climatic variability.
This study analysed the variability in local rainfall data, examined the interseasonal main and minor rainfall distribution for trends, and determined the pattern and strength of correlation with crop production in WAA to serve as future reference as well as engender policy discussions on agriculture production strategies for the area in the face of changing climate.
The district has a total land area of 1, km 2 with cultivable land area of 76, Ha. The district is endowed with large tracks of arable land suitable for cultivating cocoa, cereals, root crops, vegetables, plantain, banana, and yam. Land in the forest belt is fertile for cultivation of crops [ 41 ]. We studied rainfall records from the Ghana Meteorological Agency GMet , spanning a thirty-year period, from to Additionally, time series data on cocoyam, plantain, cassava, and tomato production and yield from MoFA for a year period was used.
This data was used to establish trends in interannual rainfall distribution and interseasonal one i. Our analysis was due to limited data from MoFA in the Fanteakwa district. In earlier assessment and rainfall impact studies on crop production, predictive models and forecasts have dominated literature. Ecophysiological model provides the aggregation of diverse components such as rainfall data and agronomic information to forecast how a particular plant will respond to different environments [ 42 ].
Ricardian or cross-sectional approach [ 43 , 44 ] is a similar model which is linked closely to a correlation between how potentially viable a particular land is and the existing agroclimatic conditions. To understand the current relationships of rainfall and crop production, the Pearson product moment correlation coefficient was used to generate statistical indices, while analysis of the interannual and interseasonal rainfall variability was done using coefficient of variation CV and precipitation concentration index PCI [ 45 ].
The PCI was used for this study because it has been the choice of model in recently published works on analysing rainfall variability and also due to its high ability to measure temporal variations in rainfall [ 46 — 50 ]. Most importantly, the PCI has the ability to describe how rainfall is distributed yearly i. Beside PCI, there are other rainfall variability measures such as the precipitation concentration period PCP , fulcrum centre of gravity , and precipitation concentration degree PCD that can equally perform well [ 51 — 53 ].
We assessed annual crop yield for cassava, cocoyam, plantain, and tomatoes for the period and rainfall data for The discrepancy of applying a standardized period between the two data sets is attributed to gaps in the production crop data.
To this extent, the period between and , spanning eleven years of rainfall and crop production relationship and trends, was cautiously used. The two data sets were subjected to bivariate correlation and cross tabulation analysis to determine the impact of rainfall variability on change in crop production.
For a minimal effect, the study analysed an annual average temperature over a period of 35 years in some instances and 27 years in the Worobong area that showed two outputs of minimum and maximum levels. Results from questionnaires that were administered and information from MoFA in the Fanteakwa district show that cropping systems and agronomic practices remained basically the same or constant in the period that the data was taken i. Analysis of monthly rainfall data from GMet between and established a pattern of variability.
Three data sets, described as Climate Assessment Decade CAD , of 10 years, ranging from to , to , and to , were categorised to allow comparison of variation in rainfall distribution in the area.
As the area experiences a bimodal rainfall regime, the CAD for each data set is further grouped into the major season, between March and July, and minor season, between September and November. This grouping was intended to examine the comparative basis for the extent of variability. The result shows a trend in seasonal major and minor variability in rainfall distribution Figure 2.
The major season for the first decade under consideration recorded an average rainfall of In other words, there was a decrease in rainfall distribution in the major seasons over the year period.
Similarly, the minor season for each CAD recorded increased variability Figures 2 and 3 , Annual average rainfall for major and minor seasons for three separate decades of , , and within the Fanteakwa District, Ghana. Source: GMet, Selected crops yield between and within the Fanteakwa District of Ghana.
Source: MoFA-Begoro, The result from the coefficient of variation CV of the major and minor seasons of the three decades showed significant variation in both seasons with the highest in the minor season CV, 5. In effect, for the year period , the total average rainfall amount reduced by mm consistently for the major season and He further evaluated the outcomes of PCI using the following indicators: values less than 10 suggest a uniform concentration; values between 11 and 20 indicate high concentration, while those above 21 are considered very high rainfall amounts.
A value for both seasons at 33 indicates a high concentration of rainfall, thereby showing enough rainfall for crop production in the 3 decades although there was significant variability over the period. The findings of the yearly crop output within the Fanteakwa agroecological zone showed a downward trend for all major crops considered except tomato Figure 2.
Just like cassava which has recorded some steady reduction from to with percentage declines of The Pearson product moment correlation coefficient analysis carried out for the major cultivated crops considered cassava, cocoyam, plantain, and tomatoes and two climatic variables rainfall and temperature revealed that rainfall negatively correlates with all crops, i.
There are visible changing trends in rainfall patterns for minor seasons spanning September to November at a higher rate than the main season in the three decades considered for our analysis in the study area.
This corroborates the finding from previous study in Ghana [ 22 ]. Such a trend shows there is a relatively less amount of rain water for crop production in the minor season in relation to previous decades and also the major season. This study did not project detailed month-on-month comparison of the data; such an exercise would have provided for each season major and minor the actual rainfall amounts relative to the corresponding months in other CAD and their values to show extent of variability.
This will further show the direct impact of monthly rainfall on agriculture for instance. In the absence of that, average yearly major and minor rainfall amount will also provide enough basis for assessing the effect of the variability on crop production in each CAD [ 20 ]. Temperature also plays a major role in determining the overall relationship between crop production and other factors such as soil, water, and technology [ 22 — 24 , 53 ].
This observation formed the basis for recommending that a lot more effort must be done to improve crop production within the major season because of less rainfall variability compared to the minor season. This strategy can help increase crop production, food security, and availability even as production in the minor season reduces.
Although the calculated PCI values states a very high rainfall concentration in the two seasons, the variability within the season was also revealed by the coefficient of variation and mean values. There were no statistically tested and proven reasons for an increase in tomato production although all other crops produced in the area suffered production loss due to climate change.
The immediate speculated reason for this increase could be the use of irrigation facilities during the off-production season [personal observation], but no study has been conducted to verify this assumption.
Besides disease, rainfall can also determine how fast a crop will grow from seed, including when it will be ready for harvesting. A good balance of rain and proper irrigation can lead to faster-growing plants, which can cut down on germination time and the length between seeding and harvest.
The crops are dependent on water during their entire lifecycle in order to survive and thrive. Soil is also greatly affected by rainfall. Additionally, as mentioned previously, overwatering or too much rain can also lead to bacteria, fungus, and mold growth in the soil. Knowing when to water, preventing disease and mold, and making sure the soil is kept at the right moisture level are all components of the overall goal of the crops and their farmers: to have the highest crop yield possible.
The right amount of rainfall can balance out these factors, which can lead to healthier, larger crops that can be harvested more fully. Balancing proper watering is key to the best crops possible. Satellites see the full range -- from high-energy gamma rays, to visible, infrared, and ScienceDaily shares links with sites in the TrendMD network and earns revenue from third-party advertisers, where indicated. Print Email Share. Boy or Girl? Living Well.
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