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force and with increasing pressure, the amount ofmoisture increased because of increasing discharge.

Conclusion

1) By changing the length of pipe, pressure andtime, the designer can choose a suitablemoisture pattern for plant age, root system andavailable pressure, and can control deeppercolation.

2) At 2 and 4 meters of water pressure, themoisture pattern is suitable for saplings and withincrease in the age of plant, a large number ofpipe sections or a high level of pressure wouldbe appropriate because in sandy soil areas rootsshould go deep to keep plant standing againstthe wind.

3) The patterns showed that the maximumhorizontal development was with 6 meter ofwater pressure and in half and quarter of pipelength. According to the adsprition low of water(10%, 20%, 30% and 40%), 6 meter of pressureis suitable pressure in this experiment.

4) Soil moisture content was calculated to bea little more than field capacity, and this is thebest moisture for plant growth.

5) In this system obstruction of one unit doesn’tcause obstruction of the whole of pipe.

6) Because of the texture of the soil used in thisexperiment, Increasing time doesn’t causehorizontal expansion but vertical expanded.

Aknowledgement

The authors are grateful to Khuzestan water andpower authority and research and standard of irrigationand drainage network office for their support.

References

Akhoonali, A.M. 1998. Determining moisture pattern forKhuzestan soils with subsurface leaky pipe irrigation.Research No. 486. Iran, Ahvaz, Shahid ChamranUniversity.

Akhoonali, A.M. 2003. Evaluation of applying porous pipewith vertical option. Research No. 406. Iran, Ahvaz,Shahid Chamran University.

Barootzadeh, M. 1999. Evaluation of the possibility of tillagepotato in sandy hills using drip irrigation. M.S.Thesis. 13-20.

Khorramian, M. and M. Mirlatifi 2000. Evaluation of Taravaporous pipe performance. Soil and Water SciencesJournal. Soil and Water Research Institute No. 2.187-188.

Camp, C.R. 1998. Subsurface drip irrigation. A reviewTransaction of the ASAE. vol. 41(5), 1353-1367.

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Fok, Yu. Si and Willardson, L.S. 1971. Subsurface irrigationsystem analysis and design. Journal of the Irrigationand Drainage Division, ASCE. Vol. (97). NO. IR3,449-454.

Teeluck, M. and B.G. Sutton. 1998. Discharge characteristicsof porous pipe micro irrigation lateral. Agric. WaterManagement. 38(2), 123-134.

Tollefson, S. 1985. The Arizona system, Drip irrigationdesign for cotton. In ‘Proc. Third International Drip,Trickle Irrigation Congress ASAE, 401-405.

Yoder, R.E. and C.R. Mote. 1995. Porous pipe dischargeuniformity. Micro irrigation for a changing world.Conserving resources/preserving the environment.Proceedings of the Fifth International MicroIrrigation Congtress, ASAE, Orlando, Florida.

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The optimal size of farm ponds in N.E. Thailand with respect to farming styleand multiple uses of water and under various biophysical and

socio-economic conditions

Penning de Vries, F.W.T.1; S. Ruaysoongnern2 and S. Wong Bhumiwatana3

Keywords: farmer support, farm modeling, multiple uses, farming style, simulation, optimization

Abstract

Water is a limiting factor on farms in Northeast Thailand because of weather and soils. Variable rainfallleads to high risks in cultivation. Catching and storing rainwater and subsequent use for irrigation reducesrisk and increases farm productivity. A practical question posed by farmer organizations is ‘what is the optimalsize of a farm pond?’ In trials, optimum values of around 12% of the farm area have been reported. Forindividual farms, however, this fraction may be much different due to features of farm soils, landscape andlocal climate, economic factors (prices of produce and multiple productive uses of water) and the preferredfarming style (i.e. the farming household minimizes inter-annual risks, maximizes income, is innovative intrying the latest techniques, or has farming as a secondary source of income).

To consider a wide range of variables and choices in a systematic and transferable manner, we developedthe farm simulation model BoNam-FS (Farm Simulation). It was used to simulate sets of scenario’s (farmingstyles, soil types, weather patterns, innovations) to determine farm performance over a range of farm pondsizes. Benefits of innovations, such as pond sealing and alternative irrigation methods, can be quantified. Theresults can be analysed with the spreadsheet BoNam-SA (Scenario Analysis) and summarized in graphs.

Results have been presented to farmer organizations to show the options for water management and howto pick a scenario that suits best a specific situation. BoNam-SA is available for further analyses, such as byfarm advisors. BoNam-FS is available for in depth analysis of farm water balances in existing or newscenario’s. For detailed explorations, specific data from the farm-in-case are needed.

1 Monsoon Asia Integrated Regional Studies, Institute ofAtmospheric Physics, Chinese Academy of Sciences, Beijing(fritspdv@mairs-essp.org), formerly: International WaterManagement Institute, Pretoria, South Africa.

2 Khon Kaen University, Khon Kaen, Thailand(sawaeng@kku.ac.th)

3 Land Development Department, Bangkok, Thailand(egd_1@ldd.go.th)

Introduction

Deforestation and unbalanced farming systemshave brought much land and water degradation inN.E. Thailand since the 1960’s, particularly by nutrientmining (Bridges et al., 2001). Several farmerorganizations (FOs) emerged in N.E. Thailand in the1980’s (Chamrusphant, 2001), either spontaneously orpromoted by NGO’s (Chutikul, 2001, Bepler, 2002).Since the early and mid 1990’s some FOs promoted theadjustment of farming practices to reverse degradation(Ruaysoongnern and Suphanchaimart, 2001). This

often included construction of a farm pond and cropdiversification. Investments for pond construction areoften arranged through revolving funds, managed bythe farmers groups, and entail loans that are repayablewithin 12 months (Bepler, 2002, Ruaysoongnern andPenning de Vries, 2005). In addition to empoweringmany individuals and setting common action agenda’s,these organizations gained a strong political influencein the government. Significant funds are now availableto speed up the expansion of the number of farmponds. Government spending will be increased furtherfor 2005-2007 as a regional drought in 2004/2005 hasgiven “water” an even higher priority. The LandDevelopment Department (LDD) advises thegovernment on implementation of the ponddevelopment scheme. The new Thai constitutionstrongly promotes participation of the people in the“conservation, maintenance and use of naturalresources” (Hungspreug, 2001), and LDD and FOssearch how to make this effective and to designrealistic loan schemes for individuals.

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Goto and Koike (1997) report that modernfarmers near Khon Kaen often cultivate 2-3 ha of land,divided into 2-3 plots and worked with 4-7 persons. Onaverage, 50% of the area is in rice and 10-15% invegetables. The authors also report that there areusually 1-2 rice crops per year yielding 2.7-3.7 t ha-1

cleaned rice of which only 10% is consumeddomestically. Vegetables, 4-5 crops per year, includebeans, cabbage and onions, are generally sold tomiddle men and yield 50-100 k Baht ha-1 yr-1. Pondwater is used to irrigate vegetables, flowering plantsand fruit trees. Some farmers produce and sell fish aswell. Farmers may also grow some trees forconvenience wood (e.g. teak, Eucalyptus) for salewhen cash is needed. An analysis of modern farmingsystems and a comparison with non-improved systemshas been carried out by Tipraqsa (2005). Cassava,a common crop in N.E. Thailand, is never irrigated andtherefore not considered in this study.

The New Theory for agricultural developmentby His Majesty King Bhumibol of Thailand (LDD,2005) underlies the thinking of FOs with respect tofarm and livelihood development. It promotesintegrated farming to achieve household food security,self-reliance and a reasonable income from agriculturalproducts from a farm with ample biodiversity anda sustainable farming system. Farm ponds are crucialin the New Theory (Figure 1).

The optimal size of a pond relative to the sizeof the farm is important but difficult to establish. It isimportant because the water surface itself is notproductive. It is difficult to establish because it dependson many factors: the crops grown (type and area), onthe prices they can fetch on the market, the localweather and soil types, and other factors. Also farmerobjectives affect the optimum ratio. The track record ofnew farms is still short (Tipraqsa, 2005), so thatfarmers have not yet sufficient experiences to be usedas a guideline. Experimentally, LDD found 12% to bean optimum (Khao Hin Sorn, 1999). In the NewTheory, a pond as large as 30% of the farm surface isproposed. It would allow full irrigation of all cropsyear round, but could require water supplies frommajor reservoirs outside the farm. Irrigation only whereand when really needed can reduce the water demandsignificantly and allows smaller and cheaper ponds.Realizing this complexity and local specificity, FOsasked scientists for help and formulated three pertinentquestions (Box 1).

The water balance on farms is complex. Farmponds receive water from rain and runoff from thefields and uncultivated surfaces. A high groundwatertable may add to filling, but drainage from ponds ismore common, particularly on sandy soils. Some farmspump groundwater for domestic and productive uses,but as a salt layer underlies the groundwater in someareas there is a threat that saline water is extracted(Srisuk et al., 2001). Roof water harvesting fordomestic water is common (Bepler, 2002); watersupply through pipes by water providers is uncommon.

Box 1. Three questions to scientists formulated ata meeting of farmer networks in N.E. Thailand,Kalasin, 20 January 2004; text in parenthesis by theauthors.

� (What are the) Land: water resource ratios for bestproductivity in different ecosystems (at farmscale)?

� (How is the) Water management for water useefficiency for each crop, both monocrops andintegrated farming system?

� (What are the) Water productivity potentials indifferent ecosystems for productivity gap analysisof each farm?

The method: simulation modelling and scenarioanalysis

When experience provides no guideline todetermine the optimum water/land ratio on farms,trials can be carried out. Such trials should last at least5-10 years and occur across N.E. Thailand due to thevariability of the weather and differences in soils. Theyare therefore slow to provide conclusions andexpensive. The LDD experiment station in Khao HinSorn carries out trials in one rainfall zone. Analternative approach, potentially much faster, witha wider range of answers, and cheaper, is throughsimulation modelling. Optimally, the modellingapproach goes together with field research andcalibration.

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In a dynamic simulation model the way howcrops, soils and ponds ‘respond’ to management andweather is calculated for short time intervals (here:one week). Calculations are repeated over periods of1-10 years. Indicators of ‘farm performance’ arederived from the computed results. With little effort,the simulations can be repeated for many scenarios offarm management to explore the options available tothe farmer. Characteristics of weather, soil, landscape,and other factors are kept as close as to the conditionsof the end user as feasible. Management is characterizedby planting dates and target yields, irrigation methodsand mulching. From the values of the indicators in thedifferent scenarios, the farmer can select the outcomethat fits his/her farm best.

This way of identifying the optimum farm pondsize makes full use of scientific knowledge and of localinsights. The modelling approach supports non-specialist with applicable scientific knowledge. Crokeet al. (2005) give examples from Australia of theimpact of modelling support to local persons andorganizations and of the improved water and landmanagement.

Several models have been published thataddress optimization on farms. These include SWB(Annandale et al., 1999), Tradeoff Analysis Model(Antle and Stoorvogel, 2000), Dam Ea$y (Lissonet al., 2003), Planwat (Van Heerden, 2004) andTechnoGIN-3 (Wolf et al., 2004). But they do notprovide all features required to answer the request ofthe FO (Box 1), particularly not with respect tomultiples uses of water, farming styles and weathervariability.

The model ‘BoNam’ for Thai farms

Outline. We developed the simulation modelBoNam-FS (bon am is Thai for ‘pond’, FarmSimulation) to support farmers across N.E. Thailand todetermine the optimum relative pond size for theconditions of any particular farm and for the preferredfarming style of its farm manager. The model is builtin the language SIMILE (Muetzelfeldt and Massheder,2003); its outputs can be analysed with MS Excel.Running the model and comparing results for differentscenario’s is user friendly. This paper presents brieflyour approach in modelling to support FOs and someresults obtained with BoNam version 3-09. A copy canbe obtained from the first author. The data used tocharacterize the farms and locations in this paper arefrom actual observations and from measurementsreported in literature. The data, however, may not berepresentative for a particularly farming situation

because we want to demonstrate possible uses incontrasting environments and did not attempt toidentify parameters from specific cases.

The actual simulation model consists of15 modules: one for the water balance of each of fivefarm plots, one for each of the three productiveactivities (vegetables, rice and fish), one for inputs withrespect to strategic decisions on farm layout and onefor operational decisions in management, one for timemanagement, two for weather and price data, and twoto track simulated farm performance (water balance,income, etc.) and annual totals. A full description of themodel is in preparation (Penning de Vries, 2006). (It istechnically straight forward to add modules for otherwater demanding activities such as for sugarcane, fruittrees, feed crops and livestock. However, the numberof farm management options for the users grow toolarge to be helpful, certainly in the early stages ofsupport in decision-making with the model. Hence weopted at first for a small set of contrasting uses of waterin BoNam. Further feedback from farmers mayindicate that another selection of activities, or indeeda larger range of water use activities, is required).SIMILE provides transparency of the model and easyinspection and modifications. Several checks anddouble accounting in the model assure consistency inmodelling processes and reduce errors. Because thefarming season begins in July and may extend well intothe next year with a second rice crop, we compute theannual totals from July onwards. To deal withuncertainty, a normal simulation run consists of9 consecutive years. This allows the user to take intoaccount carry over effects between seasons and tocalculate realistic annual averages and estimateuncertainty.

The first question about an optimum ratiobetween land and water (Box 1) can be answered byrepeating simulations for a range of pond sizes andvarying the size of the irrigated plot. The first approachis needed when a farm pond is (re-) designed, thesecond when optimizing multiple uses of water froma fixed size pond. In this short paper, we deal withvariable sizes.

Farm layout: the default farm surface area is1 hectare; other values can be specified. It consists offive sections: (i) the farmhouse, yard and the unplantedarea surrounding the pond, (ii) the pond, (iii) the ricefield, (iv) a plot with vegetables and (v) a park withtrees (Figure 2). Only the vegetable crop is irrigated, ifpossible, not the transplanted rice. Recognizing theimportance of rice production for the household, anyincrease in pond area is at the expense of the vegetable

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plot. The water balance connects the plots, if there isexcess water on the plots, it runs into the reservoir; ifthere is too much for it to contain, it runs off fromthe farm. Runon from nearby roads or field can beimportant. The dynamics of groundwater isinsufficiently known to be simulated.

Management. Management of the entire farm isconsidered rather than that of individual plots.Management is characterized by choice of plantingdates and target yields of crops planted, irrigationlevels, use of the soil amendments (e.g. bentonite) ormulch, and presence of fish. There are different stylesof managing a farm enterprise, and these lead todifferent results. About 20 parameter values andsettings in BoNam are affected by ‘style’. E.g., ifa farmer goes for a relaxed style of farming, this willbe reflected in selected crops and target yield levels,etc. We distinguish four styles:

� Style A (‘conservative’): the solid, slightlyconservative farmer who makes sure that hisfamily has always adequate rice and who prefersstability over high income. Fraction of farm inrice cultivation: 0.4. Vegetables 2-3 crops peryear. Low levels of inputs.

� Style B (‘income’): the farmer who seeksmaximum benefit and does not mind to borrowsome money to purchase rice in an unusuallydry season. Mostly commercial production inputintensive: irrigation, fertilizer, labour. Yearround production with 3-4 crops. Fraction farmin rice cropping: 0.25.

� Style C (‘scientist’): the curious, enterprisingfarmer who tries out new methods for waterdistribution, new crops, etc. Fraction farm inrice: 0.25. This style is characterized by themost efficient ways of doing things.

� Style D (‘with other jobs’): the farmer hasoff-farm income and farm activities demandlittle work and monitoring. No fish production.Fraction rice on farm: 0.5.

Performance. Simulated results of the farm areexpressed in four indicators: annual farm income,number of weeks per year that the pond is dry, quantityof water that runs of the farm, and the volume of waterused in irrigation. While it is straight forward to addother indicators to the model (e.g. the length of thedrought period in the rainy season), expressing farmperformance in these four dimensions provides alreadya large amount of output. We consider that these fourindicators give much insight into options for farmmanagement but adaptations can easily be made ifneeded.

Input data. We use weekly data of evapotran-spiration and temperature (averaged over nine years)and rainfall data (from the Mekong River Commission)for the individual years for Korat (1995-2004, in SWcorner of N.E. Thailand), Khon Kaen (1987-1996, inthe centre), Ubon Ratchatani (1961-1969, S.E.) as wellas for Nong Khai (1961-1969, N.E.). The average annualrainfall at these sites increases from 1345 via 1522 and1,673 to 1,984 mm. The pattern of cumulative valuesof the highest and lowest rainfall sites are shown inFigure 3. The duration of the rainy period at theselocations is about equal, the growing season longer inNong Khai. The potential evapotranspiration (ET0) inKhon Kaen is 1971 mm yr-1 (RID, 1994). Assumingthat this value is a fair approximation for all of N.E.Thailand, it shows that the risk of drought is significantin all locations particularly at the beginning and end ofthe growing season, particularly in the southeast. Wedo not address the question whether the climate of N.E.Thailand is slowly getting drier and the rainy seasonoccurring later, but the eventual consequences of suchchanges could be explored through modelling.

Data from the soil map of Thailand could not beused for these simulations since the scale is too small.We characterized the rice soil on the farm as ‘clayey’and the soil on the vegetables plot as ‘loamy’ (Penningde Vries et al., 1989). Simulations apply for conditionswithout runon from outside the farm and water supplyby providers. Drainage from the pond is set at a smallfraction of its contents per week (0.5%). Soil fertility

Figure 2. Approximate layout of the five plots on a farm.Single arrows show the direction of flow of runoff water.The double arrow suggests that this border between thevegetables plot and the pond can be moved betweensimulation reruns

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is addressed implicitly in crop growth. The landscapeof the farm is characterized by runoff patterns betweenthe plots. For this paper, we did not pursue simulationof farms with sandy soils as their rate of drainage ishigh and building a pond not economically attractive.Moreover, even within a sandy area, heavier soils arepresent on parts of many farms.

Crop simulated have characteristics of ‘lowlandrice’ and of ‘bean crops’ in their development andgrowth parameters (Penning de Vries et al., 1989).Target yields are set by farming style and reflectvariety choice and fertilizer level. Fish grow 10% perweek if sufficient feed is supplied (related to ‘style’).Common market prices (2004) are used for theproduce. Only gross income is computed.

Calibration. Since it is impossible to collectsufficient basic data for each case for which the modelwill be applied, it is good practice to collect data froma number of trials and to compare them with actualobservations for the purpose of calibration. Much ofthis still needs to be done and ‘calibration’ is so faronly done by ‘guestimation’; results should thereforebe taken as indicative values only.

Outputs

SIMILE allows inspection of the values of allmodel variables at any time interval in output tables orgraphs. Export of these variables is possible for furtheranalysis with other programs, such as MS-Excel.The set of macro’s for Scenario Analyses is calledBoNam-SA.

Looking in great detail to individual variables isnecessary when the model is build and tested. Acomprehensive set of outputs could include weekly ormonthly values of the soil relative water contents in theplots 3 and 4, levels of water stress in the crops, crop

dry weights, and runoff between fields and from thefarm, and the water level in the pond.

In the productive stage of modelling, however,one is interested in a few outputs only, such as theindicators of performance. We use four indicators offarm performance: (i) annual farm gross income(Baht yr-1) as it results from rice, vegetables and fish;(ii) number of weeks per year that a pond is dry asa measure of risk; (iii) the quantity of irrigation applied(m3 yr-1) that can be used to determine water useefficiency, and (iv) the quantity of runoff from the farm(m3 yr-1) to see the consequences of choices onneighbouring farms. In the next section, we provideexamples of the first two indicators.

Answering the questions

The question about the optimum size of the farmpond (question 1, Box 1) is relevant before a majorinvestment of excavating a pond or constructinga reservoir and for detailing the conditions for a loanfor this purpose. It can be answered by running themodel for scenarios of relative pond sizes, and byinviting the farmer to select the answer that fits her/him. For an illustration of the type of results provided,we ran the model for relative pond sizes from 0.0 to 0.3(m2 pond surface m-2 farm) and for the four farm styleswith otherwise the same input data and all managementparameters set at default values. A first round ofinteractions with the FO Local Wisdom took place inBuriram (2004), and other rounds are planned.

The following graphs present three values forthe indicator at each pond size. The middle onecorresponds with the average level, the upper one withthe level attained or exceeded in the best 25% of theyears and the lower one with the level attained or notquite in the worst 25% of the years. The spreadbetween the upper and lower values is a measure of theinter-annual variability in weather. Irregular patterns inthe graphs are due to non-linear processes, such asfailure or a second crop due to drought.

Results in Figure 4 for the indicator ‘farmincome’ for the driest site (Korat) indicate that a pondsize of 5-10% is optimal for the styles A-C, while‘no pond’ is usually best for style D. There is no sharpoptimum, and the value tends to be higher for thewetter years. Note also the difference in Y-axes: thestyles B and C provide more income than style A andmuch more than D. The main reason why the incomegoes down at larger pond sizes is that the size of thevegetables plot, a major income earner, becomes small.

Figure 3. Cumulative average weekly values ofprecipitation for Korat and Nong Khai, representing thedriest and wettest locations in N.E. Thailand, respectively

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style B (‘income’). It is clear that the chance of thepond running dry for several weeks a year is prettylarge. The reason that the risk increase in Korat forlarge pondsizes is that the catchment area of waterbecomes too small. In Ubon, 15% of the farm area inpond keeps ample water for irrigation year round.Quite obviously: the best recommendations for sizesand farming practices between these regions differsignificantly.

To demonstrate the value of the model forexploration of innovations, we reran the model for thesame situations as in Figure 5, but reduced drainagefrom ponds to zero (as could be done with clay on thepond bottom, or with plastic sheets). The results areshown in Figure 6. The difference is not as large asmight be expected because evaporation losses from thesurface are as large as the (low) rate of drainage inFigure 5.

Figure 4 (a-d). The relation between pond size andaverage farm income in Korat under four farmingstyles

To illustrate the large difference betweendifferent parts of N.E. Thailand, we show in Figure 5the indicator ‘number of dry weeks’, and give anindication of ‘risk’ related to climate. Selected are datafrom 2 locations (Korat, Ubon) and all for farming

Figure 5 (a, b). The number of weeks that the pond isdry under farming style B in Korat and in Ubon

Discussion

It is important to realize that in any situation the‘optimum pond size’ is not the same for each of thefour indicators. Moreover, the different styles, farmersgive different weights to each of the indicators. Asa result, determining ‘the’ optimum for a particularfarmer cannot be done by reading only Figures 4, 5or 6, and was suggested for brevity in the previoussection. A more comprehensive set of graphs needs to

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be considered together in a multi-criteria analysis.How this can be done effectively and interactively forthis model with Thai farm advisors has still to beestablished.

This sample of results demonstrates some of thecapacities of the BoNam model and present onlyindicative values. Limited calibration and feedback,however, are still a handicap to the practical value ofthe results. On-farm observations and interactionsessions are planned for the near future.

Shape of the pond was not mentioned so fareven though farmers are interested in this feature. Theshape of the pond (oval, rectangular, multiple smallones) does not affect much the results of simulationsand any particular choice won’t affect the resultssignificantly. Practical advice, therefore, is to identifythe shape(s) in agreement with landscape (lower partsof the farm and following the landscape). In low parts,upwelling of water from local acquifers could be veryimportant. A factor farmers can influence is shading ofthe pond. This leads to a reduction of wind and henceof surface evaporation (but an increase of transpirationif trees provide shade) to reduce evaporation.

Local runon can be very significant. Yet, weobserved on several occasions and different farms thatfarmers tend to overestimate the importance of rainfallin filling farm ponds and underestimate run-in; thequantities of drainage and upwelling are difficult to

appreciate. A benefit of using the BoNam model cantherefore be the refinement of the mental models FOsand farmers to in their daily operations with the farmwater balance.

A model can also help to address consequencesof variability and heterogeneity that otherwise can bedifficult to judge. We avoided use of averaged rainfalldata for simulation as Nonhebel (1994) found that useof such data may lead to overestimation of yields by50% (unirrigated) to 15% (fully irrigated crops).However, there is still significant uncertainty in otherimportant parameters, so that evaluation andcalibration in the future needs more attention.Metselaar (1999) demonstrated clearly the importanceof parameter inaccuracy in predictive modelling, andshowed that calibration is indispensable beforepractical results can be obtained.

While it may be argued that the pond on everyfarm is customized in size and shape, in practice thiscannot be handled at a large scale by the contractorswho build them. They provide only the standard sizeand shape, where farmer can only determine where onthe farm and, within limits, the linear. On a 1 hectarefarm, the LDD standard volume (fixed value of1,260 m3) corresponds with a relative pond size of 4%(or about 15 × 25 m). Future scenario analysis shouldconsider how to optimize for every growing season theuse of the water in a pond of fixed size.

Acknowledgements

We thank the Farmer Organization ‘LocalWisdom’ for inspiration and advice, and a reviewer forvaluable comments.

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Figure 6 (a, b). The number of weeks that the pond isdry under farming style B but with sealed ponds to stopdrainage

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