&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;A simulation model to predict the daily fluctuations of soil moisture&nbsp; content was developed using the Versatile Soil Moisture &nbsp;Budget (VSMB) equation by employing pascal computer language.&nbsp; The model was tested against actual &nbsp;field data and showed fairly accurate results.&nbsp; The correlation coefficient 0.99 

A. Ali, Mohammed

D. E1fadil, Abdelkarim

I. Mohammed, Hassan

English

Journals Of University Of Gezira

Gezira J. of Agric.  Sci. 2(2): 131-142(2004)                                                    131  Soil Moisture Contents as a Determining Factor  Machinery Selection 1. Development of computerized simulation model for the  prediction of daily fluctuations in soil moisture content  Abdelkarim D. E1fadi1I  Mohammed A. Ali1 and Hassan I. Mohammed2  1. Department of Agricultural Engineering Faculty of Agricultural Sciences, University of Gezira, Wad Medani, Sudan 2. Department of Agricultural Engineering, Faculty of Agriculture, University of Sudan for Science and Technology, Shambat, ABSTRACT        A simulation model to predict the daily fluctuations of soil moisture  content was developed using the Versatile Soil Moisture  Budget (VSMB) equation by employing pascal computer language.  The model was tested against actual  field data and showed fairly accurate results.  The correlation coefficient 0.99 .  INTRODUCTION  The major problem in all semi-arid regions is the short time available  for tillage.  This is due to the fact that under arid conditions the seeds should be sown as soon as possible after the rainy season begins to attain  the full benefits of the vegetation period; and any delay shortens the growth period of the crop, because ripening begins anyhow with the onset of the dry season, resulting in yield losses and lower crop qualities. Soil tillage cannot, however, begin until the rainy season starts, and if tillage is carried out too early, the soil surface may slake, reducing the infiltration of rain water and causing surface runoff (Krause and Lorenz, 1984). Clay soils, under rainfed agriculture, can be worked with a relatively low input of energy only during a short period after the first rains because the water content of the topsoil changes very rapidly.  When the clay soil is too dry it may become rock hard, and when too wet it may not be workable (Allman and Knoke, 1947).    Gezira J. of Agric.  Sci. 2(2): 131-142(2004)                                                    131  Another problem is that the crop yield under arid conditions is highly dependent on rainfall. Below average rainfall may result in complete crop failure, which causes complete loss of the inputs of soil tillage, seeds and labour, and leaves the bare soil virtually without protection against the wind and sun, thus making it extremely susceptible to erosion.       A third problem is that productivity in arid regions is decreasing  annually because of the decreasing amount of rainfall, extensive rotations as well as financial constraints.      Any attempt to estimate the quantity and availability of soil moisture content must be based on a thorough understanding of the dynamic balance of water in the soil. The soil water balance (  W) is the difference between the amount of water added (Wa) and the amount of water withdrawn (W w) during a certain period of tinie as given by following equation (Simalenga, 1989(:  W     = Wa-Ww ………………………………………(1)      Rain or irrigation water constitutes the main source of water added to the soil. Part of this water will be lost as surfae runoff depending on the land slope, grass cover, soil type and rain intensity; part will evaporate directly from the soil surface; some is taken up by plants for growth some may percolate deep into the soil beyond the root zone; whereas remainder adds to the moisture storage at the root zone (Hillel, 1980).      Several models for estimating soil moisture, utilizing hydrological data, have been reported (Shaw, 1963; Ligon et al., 1965; Nath and Johnson, 1980).      Witney and Oskoui (1982) modified and simplified the soil moisture balance equation to the following form  :  M = Mp + pr-R Pe-ET……………………(2) Where  :  M =  Soil moisture content (mm(. Mp = Soil moisture content on previous day (mm( Pr =  Precipitation (mm(.  Gezira J. of Agric.  Sci. 2(2): 131-142(2004)                                                    133  R =  Runoff (mm(. Pe =  Percolation (mm). ET = Evapotranspiration (mm(.  In order to apply Equation (2), its components can be determined as follows: a) Evapotranspiration (E T)        Direct measurement of evaptranspiration can be made by lysimeter Schwab et al, 1966), but it is expensive. On the other hand, Fadl (1978) used the neutron probe for the daily measurement of soil moisture and it showed an erratic pattern of ET which is also expensive and dangerous. Moreover, Farbrother (1969) published relative turgidity curves to estimate ET from irrigated cotton fields in the Gezira Research Station. Many recent studies have advocated using the Penman-Monteith equation for estimating ET (Doorenbos and Bruitt, 1975). The equation states that: PET={       (Rn-G) + {Y / + Y} (15.36) (1+0.0062S2) (es-ea)..(3)  Where: PET = Potential evaptranspiration (cal-cm-2 –day(.  = Slope of saturation vapor pressure (mb-˚C(.   =  Radiation reflection coefficient (0.05 for water Surfaces and 0.2 for green crops). Rn = Net radiant energy available at the surface (calcm-2-day-l(. G = Energy into the soil (cal-cm-2-day-l(. S2 = Average wind velocity at mean dewpoint temp. )Mb-0C(. es = Mean saturated vapor pressure (mb(. ed = Saturated vapor pressure at mean dewpoint temp. (mb-0C). McCulloh (1965) modified the Penman-Monteith equation so as to incomorate the effect of altitude, and the Penman tables have been recalculated from the following equation: PET = { / +   }{        (0.02 Cos            /    { {   Ta4 = (0.10-0.9n/N)(0.56-0.08ed0.5)}+{      } {0.26(1+h/20000)(1+u/100)(     )……………………..(4) Gezira J. of Agric.  Sci. 2(2): 131-142(2004)                                                    131  where: Ra=Radiation of the outer limit of the earth atmosphere(cal-cm-2day- l(.  =Radiation reflection coefficient (0.05 for water surfaces and 0.2 for green crops(. n/N = Ratio of actual. to possible hours of sunshine.    Stefan-Boltzman constant (11.7 x 10-8 cm/day(. H = Altitude (m(. U =.Daily wind run at a height of 2m (km/day). 𝜙   Latitude. Ta = Ambient air temperate (K = C+273).      French studies (Gosse et al., 1977; Katerj and Perrier, 1983) have used the Penman-Monteith equation and advocated its use throughout France for direct computation of crop evapotranspiration.  Actual evapotranspiration (AET) can be obtained by applying some correction factors to the potential evapotranspiration, such as soil factor surface cover factor and rainfall distribution factor (Simalenga, 1989) as follows:    AET = (PET)(kd)(ks)(kr)………………(5)  Where: AET= Actual evapotranspiration kd =   Soil dryness factor ks = Surface cover factor kr = Rain distribution factor      Adam and Farbrother (1977) found that for bare soil the soil dryness factor (kd) is 0.55.      The soil surfaces cover factor can be determined by the following equation (Gerb, 1996(: ks = 1-0.005PSC…………………..(6) Where : PSC = Percent soil cover.      The rainfall distribution factor (kr) was developed by Schwab et al. (1966 )  as shown in Table (l(.  Gezira J. of Agric.  Sci. 2(2): 131-142(2004)                                                    131  Table l. Rainfall correction factor (Schwab et al., 1966(. Number of consecutive days without rain Kr  0 I .00 1-2  5.75 3-5  5.65 5-above 5.55   ) b) Runoff (R)     Runoff is that portion of the precipitation that makes its way toward streams, lakes, channels or oceans. The runoff volume is affected by rainfall intensity and the watershed characteristics such as size, slope, orientation or topography, geology and surface cover. There are different methods for estimating runoff volume, but USDA-SCS equation and the runoff number techniques were considered to be most accurate (Hudson, 1975 ) ). The equation developed in this regard states that:  = (Ra-0.2S)2/(Ra+0.85)…………………….(7) Where  :  Q = Direct runoff (mm(. Ra = Rainfall (mm.) S = Maximum potential difference between rainfall and runoff (Watershed storage parameter, mm). In this equation (S) is determined as a function of the runoff curve number (RCN) such that  :  S=  25400/RCN -254………………………………………….. (8)         RCN varies between 0 and 100 depending on the soil type, infiltration properties, vegetation cover, antecedent moisture content and land use practices; and it can be determined from Table (2).       Gezira J. of Agric.  Sci. 2(2): 131-142(2004)                                                    131  Table 2. Determination of RCN (Schwab et al., 1966(. Land user or cover  Treatment or Practice Hydrologic condition Hydrologic soil group   A B C D Fallow row crop Straight row - 77 86 91 94 Row crop "         " Poor 72 81 88 91  "         " Good 67 78 85 89  Non contoured Poor 75 79 84 88  Contoured Good 65 75 82 86  Terraced Poor 66 74 85 82  Terraced good 62 71 78 81 Small grains Straight row Poor 65 76 84 88  Straight row Good 63 75 83 87  Contoured Poor 63 74 82 85  Contoured Good 61 73 81 84  Terraced Poor 61 72 79 82  Terraced Good 59 75 78 81 Close seeded Straight row Poor 66 77 85 89 Legume or Straight row Good 58 73 81 85 Rotation meadow Contoured Poor 64 75 83 85  Contoured Good 55 69 78 83  Terraced Poor 63 73 85 83  Terraced Good 51 67 76 85 Pasture or range " Poor 68 79 86 89  " Fair 49 69 79 84  " Good 39 61 74 85  Contoured Poor 47 67 81 88    (c) Percolation (Pe)      Percolation refers to the entry of water into the soil. It is the source of soil moisture to sustain the growth of vegetation and the ground water supply of wells, springs and streams (Schwab et al., 1966). Percolation affected by the soil type, vegetation cover, land slope as well antecedent moisture content.      Hunt (1986) indicated that the amount of free water percolated per day is inversely proportional to the wilting point and directly proportional to the moisture above field capacity. Gezira J. of Agric.  Sci. 2(2): 131-142(2004)                                                    131       A more simplified method for estimating percolation was developed by Hunt (1986), as follows  :  D = Free water above field capacity/2PWP…….. (10) Where  .  PWP = Permanent wilting point (mm(.      The objective of this research is to develop a computer model to Estimate the soil moisture content at the top 300 mm of the soil. MODEL DEVELOPMENT       In order to develop a computer model to predict the daily fluctuation of soil moisture content, the water balance equation (I) was used.  The components of the equation were modeled using Pascal language, and the input data were as follows  :  )l) Soil physical properties (FC,PWP, and soil type), initial moisture content and some correction factors (kr, ks and DC). ( (2  Record of the daily rainfall and potential evapotranspiration. The general features of the model were as depicted in Fig. l, and can be summarized as follows  .  )l) The model works in English and uses SI units    (2)  RI-he basic structure of the model is fairly typical, since there is a  menu to allow the user to enter the initial data.  (3 ) Input data entry is made directly to the screen. (4 )  Output data will be displayed on the screen and they include the AET, runoff, percolation and moisture content. Initially, evapotranspiration is obtained by entering the meteorological data which are radiation, hours of sunshine, wind velocity, saturation vapor pressure and energy in the soil.     The operational steps of the soil moisture prediction model were as  follows  :   Gezira J. of Agric.  Sci. 2(2): 131-142(2004)                                                    131    Step I:     This involves entering the SMp, soil physical properties (FC,PWP), PET, rainfall and correction factors and constants. Then the model will calculate the AET according to equation (5). The correction factors used were kd = 0.55,ks=1 and kr=0.55 according to Table 1. Step II:     If the amount of rainfall is enough to cause runoff, then the in model will calculate the runoff from equation 7. RCN was determined from estimates of the Schwab et al. (1966( as Gezira J. of Agric.  Sci. 2(2): 131-142(2004)                                                    131  shown in Table 2.      The model will determine the value of the maximum potential difference between rainfall and runoff as follows  :  S = (25400/89)-254 = 31.39 step III: The  model, then calculates percolation according to equation 10 step IV:  From SMp, AET, Ra, D and runoff the model will calculate the soil moisture content of a specified day. MODEL VERIFICATION      In order to verify the ability of the model to predict the SMC, it  was tested against the moisture depletion curve developed by Adam and Farbrother (1977) for irrigated clay soils.      By assuming a field fully saturated with water (140mm), daily PET )from Wad Medani Meteorological Station) was entered in the model for 18  rainless days. The model evaluated the SMC on daily basis.  The results were plotted against the moisture depletion curve of Adam and Farbrother (1977). The comparison between the predicted and measured values of SMC (Table 3) showed very close agreement. The result of correlation analysis between the predicted and measured data gave a correlation coefficient of 0.99. This proved that the model was able to predict the soil moisture content with a high degree of accuracy. Application and limitations of the model      It has been shown that the water balance equation can be used to estimate SMC. To change the model to estimate SMC at another location with different soil type, different parameters will have to be calculated before use in the model for the new location and soil characteristics.       Actual field tests have to be conducted to see if the assumptions made during the model development are valid for another locational with similar soil type before using the model   Gezira J. of Agric.  Sci. 2(2): 131-142(2004)                                                    111  Table 3. Measured and predicted SMC Days after saturation Measured SMC (mm) Predicted SMC (mm) 2   4 132.0 155 6 104.0 85 8 80.6 75 10 64.0 63 12 50.6 45 14 42.2 35 16 38.8 35 18 25.2 29 20 19.5 28  CONCLUSIONS      From the  results of this research work, the following conclusions can be drawn:  ) l) It was possible to develop a computer model to evaluate daily changes of soil moisture content. (2 )  Assumptions regarding the use of the developed model, concern-ing  soil dryness. surface cover and rainfall distribution factors proved to be valid for the case under study. (3) Using the Versatile Moisture Budget equation, the daily changes of SM can be predicted fairly accurately from the model using the soil characteristics and weather data. (4)  The model as tested against actual field data and proved to be fairly a curate. REFERENCES Adam H.S. and H.G. Farbrother. 1977. Crop water use in irrigated and rainfed agriculture in the Democratic Republic of Sudan. Paper presented at the United Nations water conference, Mar Del Plata, Argentina. March 1977. Allman, M.S. and Knoke. 1947. The pf of the soil moisture at the wet limit of plowing range.  American Society of Soil proceeding 12: 22-23. Doorenbos, J. and W.O. Bruitt. 1975.  Guidelines for predicting crop water requirement, irrigation and drainage paper no. 24, Food and Agriculture Organization of the United Nations, Rome, 179p. Fadl, O.A.A. 1978. Neutron probe measurement of evapotranspiration Gezira J. of Agric.  Sci. 2(2): 131-142(2004)                                                    111  Gezira Research Station. Annual Reports. Wad Medani, Sudan. Farbrother, H. G. 1969. Cotton Research Annual Reports. K London. UK. Gerbs B. W. 1966. The effect of surface applied wheat straw on soil water losses by solar distillation. Soil Science Society of America Proceedings 30: 786-788. Gosse, G.A., A. Perrier and B. Itler. 1977. An International Commission of Irrigation Development bulletin 43 (2): 1-34. Hillel. D. 1980. Fundamentals of Soil Physics. Academic Press Inc. Orlando. Florida 32887. U.S.A. Hunt. D.R. 1986. Engineering Models for Agricultural Production. AVI Publishing Company Inc. Westport.. Connecticut. USA. Hudson, N.W. 1975. Field Engineering for Agricultural Development. Clarendon, Oxford, UK. Katerj, N. and A. Perrier. 1983. An International Commission  of  Irrigation Development Bulletin 43 (2): 1-34. Krause, R. and F. Lorenz. 1984. Soil tillage in the tropics and subtropics, GTZ. Dag. Hammerskjold-Weg I -2, D6236, Eschborn l. Germany. Ligon T.J., G.R. Beniot and A.B. Elam. 1965. Procedure for American soil moisture deficiency and excess. Transactions of the Society of Agricultural Engineering 8 (2): 219-222. McCulloh, J.S.G. 1965. Tables for the rapid computation of the Penman estimates of evapotranspiration. East Africa Agriculture and Forestry Journal 2: 286-295. Nath. S. and W. H. Johnson. 198(). Development of soil moisture model to predict soil moisture and tractability for harvesting. Agricultural Mechanization in Asia, Africa and Latin America. (AMA) 11(1):73-78. Schwab G.I. R.K. Frevert, T.W. Edminster and K.K. Barnes. 1966. Soil and Water Conservation Engineering. 3rd Edition John Wiley and Sons Inc. New York. London. Shaw, R. H. 1963. Prediction of soil moisture under oats for spring Period. Lowa State Journal of Science 37(4): 417-424. Simalenga, T. E. 1989. Simulation model to predict field workdays and its use in machinery selection under tropical conditions. Ph.D. Thesis. Royal Veterinary and Agricultural University, Agrovej, Denmark. Witney, B. D. and K. Eradat Oskouki. 1982.  The basis of tractor power selection on arable farm. Journal of Agricultural Engineering Research 27:513-527. 

Soil Moisture Contents as a Determining Factor  Machinery Selection

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Soil Moisture Contents as a Determining Factor Machinery Selection

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