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Agricultural Systems 115 (2013) 83–94
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Agricultural Systems
journal homepage: www.elsevier .com/locate /agsy
Agrarian change, agricultural modernization and the modelling of agriculturalhouseholds in Tibet
Colin Brown ⇑, Scott WaldronSchool of Agriculture and Food Sciences, The University of Queensland, QLD 4072, Australia
a r t i c l e i n f o
Article history:Received 26 January 2012Received in revised form 6 September 2012Accepted 11 September 2012Available online 23 October 2012
Keywords:Agrarian changeAgricultural modernizationWestern ChinaRural livelihoodsTibet
0308-521X/$ - see front matter Crown Copyright � 2http://dx.doi.org/10.1016/j.agsy.2012.09.008
⇑ Corresponding author.E-mail addresses: [email protected] (C. Brow
(S. Waldron).
a b s t r a c t
Powerful forces of agrarian change are at work in western China while the government has stepped upefforts to ‘‘modernize’’ agriculture. Major components of the modernization process are to disseminateimproved crop and livestock breeds and adjust changing agricultural structures including a shift from sta-ple food crops to more specialized crop-livestock systems. This paper explores the changing role of agri-culture and the impacts of new agricultural structures on household livelihoods through a detailed modelof farm households. The model aids understanding of the complex dynamics and choices faced by farmhouseholds that consume much of their own food production but who are under great pressure to spe-cialize and engage in more commercial activities both on- and off-farm. The model draws on detailedinformation and case studies in Tibet, a region that reflects the marginal productivity, ethnic diversity,rudimentary market systems and development challenges of much of western China. The model resultsdemonstrate that even in the context of agrarian change, agriculture continues to play a significant role inthe livelihoods of these Tibetan farm households. They also highlight how mooted specialization paths,despite significantly increasing household returns, fundamentally change the nature of these farm andhousehold systems and risks faced by these households. The detailed modelling enables identificationof tight labour constraints, feed gaps and other changes to farm and household systems brought aboutby the specialization. Such information is crucial in guiding refinements to marketing systems and insti-tutional and policy settings needed to strengthen and smooth out the process of agrarian transition inwestern China.
Crown Copyright � 2012 Published by Elsevier Ltd. All rights reserved.
1. Introduction
Western regions of China present a development conundrumfor the Chinese government. Despite huge investments and infra-structure programs, the rural sector in western China has not expe-rienced the transformation it has in eastern China. Householdincomes remain low while the gap between rural and urban in-comes widens and ethnic tensions in parts of the region persist.Although the emphasis remains on infrastructure development,the Chinese government has also invested heavily in recent yearson agricultural modernization with a focus on breeding, new agri-cultural practices and new agricultural structures. These develop-ments raise a series of questions including: what role doesagriculture play in the livelihood of farm households?; to what ex-tent does agrarian change impact on these households?; and howdo agricultural specialization and changing enterprise mixes im-pact on household incomes, livelihoods and farming systems?
012 Published by Elsevier Ltd. All
To address these questions, this paper reports on a detailedmodel and micro-level analysis of agricultural households in Tibet,an area that reflects many of the challenges confronting westernChina. Traditionally, rural households relied heavily on agriculturefor their food, fibre and fuel on the remote, high-altitude Tibetanplateau. Commercial exchanges were modest and markets rudi-mentary. Although agrarian change has not taken place in Tibeton the scale observed elsewhere in China or in much of SoutheastAsia, the nature of agriculture and rural society is changing. Pow-erful drivers of change—including industrialization, urbanization,migration, off-farm and non-farm employment opportunities, thedevelopment of markets and cash economies, and agriculturalintensification—are all gathering momentum in rural Tibet.
Various studies have highlighted the role of livestock in devel-opment including integrated crop-livestock systems and moreintensive dairy systems for small holders (Delgado et al., 1999;McDermott et al., 2010; Udo et al., 2011). The push to more inten-sive dairy systems and shift to fodder crops has also been evidentin Tibet especially since attainment of per-capita grain productiontargets in the late 1990s. However there is a need to examine thislivestock development in the context of agrarian change and
rights reserved.
84 C. Brown, S. Waldron / Agricultural Systems 115 (2013) 83–94
particularly with respect to the demands on household labour andengagement with external markets.
This paper first highlights the macro-level changes occurring inrural Tibet. It then describes a model used to describe and analyzefarm household systems in the main agricultural areas of Tibet. Themodel draws upon previous surveys of Tibetan farm households aswell as detailed farm household case studies to develop a flexibletool capable of examining a range of household systems and policyand market scenarios. Based on this model analysis, key features ofthese systems are then reported. The model is also used to explorethe potential impacts on household consumption, resources, andeconomic returns of the push into more intensive dairy systemsand associated partial shift away from staple food crops to foddercrops.
2. Agrarian transition, macrodevelopments and drivers ofchange
2.1. Changing developments
In Tibet, a growing population in rural areas combined withlimited agricultural areas has placed great pressure on using farmland more intensively. Chinese official statistics report natural pop-ulation growth rates in Tibet of 10.3% or twice the national averagepartly due to relaxed family planning policies for ethnic minorities.(The aggregate statistical data presented in Section 2 are sourcedfrom various issues of the China Statistical Yearbook and Tibet Sta-tistical Yearbook.) In 2009, there were 460,000 rural householdsand 1.2 million agricultural workers compared with 313,000 ruralhouseholds and 0.8 million workers in 1980. The general migrationfrom rural to urban areas partly offsets the increased populationgrowth rates in rural areas. The proportion of rural population inthe total population decreased from 84% in 1990 to 76% by 2009,but the movement is less pronounced than elsewhere in China.The pressure is exacerbated in areas resettled by poverty house-holds or by pastoral households as the result of grazing restric-tions. While the merits of these schemes is a moot point(Richard, 2005), as they involve reclaimed marginal agriculturalland, there is a need to identify the farming systems that can bestsupport the livelihoods of the re-settled and existing households.
Fig. 1. Source of income for rural households in Tibet: 1990, 2000 and 2010. a Values in ba(2011).
Population pressure also impacts on per capita incomes.Although per capita rural incomes of Rmb4139 in Tibet in 2010 werethree times higher than in 2000, and nine times higher than in 1990,they are only two-thirds of the national average and consistentlyrank among the lowest of all provinces and autonomous regionsin China (China Statistical Yearbook, various issues). (In 2010, onaverage, there were 6.77 Rmb to 1 USD.) Furthermore the gap be-tween urban and rural incomes is among the highest in China withrural net per capita incomes only 28% of disposable urban per capitaincomes in 2010. Thus extreme pressure exists on all levels of gov-ernment to increase incomes and investment in rural Tibet.
Fig. 1 highlights broad sources of rural household net incomes inTibet through time. Net per capita incomes for rural households inTibet rose markedly from 1990 to 2000 and more so from 2000 to2010. Wage (off-farm) income increased during the 1990s from vir-tually 0% to 22% of rural household incomes. Income from remit-tances also increased during the 2000s and accounted for 15% ofnet incomes by 2010. Many farm households in Tibet engage insome off-farm work covering the full gamut from long term migra-tion, to temporary or seasonal migration, and casual irregularemployment to supplement their incomes. Off-farm income op-tions, however, are more constrained in Tibet than other moredeveloped and favourably located agricultural areas in China andare most available in summer. Most off-farm income for Tibetanagricultural households in Tibet involves specialized activities suchas the gathering of ‘‘caterpillar fungus’’ (genus Cordyceps) as well asconstruction. Collection of the high value fungus from specific re-mote locations is a family activity from May to June. Originally anunregulated and lucrative activity, widespread picking has de-graded mountain grasslands, and concern over resource rentextraction by external parties has seen changes to access and feesthat decrease the attractiveness of this activity. Construction is lim-ited in Tibet to April to September, and is influenced by the vagariesof the economic conditions and government-initiated constructionprograms. Construction is undergoing transition in China withmany construction companies employing external teams of semi-skilled labourers rather than unskilled local workers.
Development funds from the central government to Tibet havetargeted major infrastructure. Despite significant impacts on urbangrowth, rural households have received relatively little flow-on
r graph represent proportion of household income. Source: Tibet Bureau of Statistics
Table 2Overview of Bailang, Naidong and Mozhugongka counties – 2009. Source: TibetBureau of Statistics (2010).
Mozhugongka Naidong Bailang
Population 45,866 58,514 44,880
Rural labourers (persons) of which: 15,665 20,324 21,563– Farming, animal husbandry,
forestry, fisheries11,759 973 13,757
– Industry 27 243 1175– Construction 1130 5315 3611– Other non-agricultural 2744 5063 3020
Gross value agricultural production(1000 Rmb) of which:
1691 1300 2255
– Farm 1343 653 936– Animal husbandry 321 221 1299
Gross value industrial production(1000 Rmb)
105 5747 5022
Sown area (ha) 5250 4189 8184Irrigated area 3338 3779 8184Cereal area 3794 2591 5771Oilseed area 1052 621 879
Cereal production (1000 tons) 2234 2134 3945Oilseed production 277 155 234
Large animals (1000) 137 55 45Sheep and goats 103 93 214
Meat output (tons) of which: 7468 2427 1276– Beef output 6621 1276 379– Mutton output 582 453 755
Milk production (tons) 3090 2102 3898Sheep/goat wool (tons) 42 56 116
C. Brown, S. Waldron / Agricultural Systems 115 (2013) 83–94 85
benefit. Indeed considerable debate occurs over whether infra-structure programs have widened the income and developmentgaps between rural and urban residents and between ethnic Tibet-ans and non-Tibetans due to differences in skills and resources.Thus, as part of both agricultural modernization and broader devel-opment programs, more government attention is being devoted toagriculture. To date the focus has been on facilities and breedingprograms with less attention on value chains and marketing sys-tems, extension programs, and institutional structures needed to‘‘modernize’’ the sector. Tibet Daily (2009) reported plans by theTibet Autonomous Regional Government to invest Rmb2.86 billionon agriculture and livestock breeding. Yields for grain crops havedoubled and for canola trebled since 1980, although they rose byjust over 10% from 2000 to 2010 at a time when chemical fertilizerapplication rose from 25 kt in 2000 to 47 kt in 2010. The extent ofcommercialization has increased but slowly. Grain sold as a pro-portion of grain produced rose from 11% in 1995 to 14% in 2010.
The structure of Tibetan agriculture has also changed as high-lighted in Table 1. Animal husbandry accounted for two-thirds ofthe value of agricultural output in Tibet before 1980 but the sharefell during the 1980s and 1990s to less than half. The fall was takenup by cropping whose share rose during the 1980s and 1990s, pri-marily in response to policies aimed at increasing grain productionand self-sufficiency. Although the total area cultivated in Tibet var-ies little year-to-year at around 229,000 hectares, the mix of cropshas changed. The proportion of grain crops in sown area fell from87% in 2000 to 71% in 2010. Some of this area was taken up by oil-seed crops—which increased their share from 7% to 10%—but fod-der crops rose fourfold from 2000 to 2010 mainly in response togovernment promotion of fodder crops following Tibet’s attain-ment of per-capita grain production targets in the late 1990s. Forlivestock, stable overall numbers from 2000 to 2010 masks thesubstitution from small ruminants (sheep and goats) to large rumi-nants (cattle and yaks). Bovine numbers increased 41% from 1980to 2010 while small ruminant numbers decreased 14% over thesame period. Bovine meat output as a proportion of total meat out-put increased from 45% at the start of the 1980s to 50% at the startof the 1990s to 64% by 2010. Although the number of animals re-mained constant from 2000 to 2010, meat output increased 76%from 149 kt to 263 kt, reflecting the livestock mix and somecommercialization of the livestock sector, albeit from a low base.Table 2 highlights the relative importance of the various agricul-tural activities and relative to other activities for the counties fromwhere the case study farms were located.
Table 1Changes in crop and livestock sectors in Tibet – 1980–2010. Source: Tibet Bureau ofStatistics (various issues).
1980 1990 2000 2010
CropsSown area (1000 ha) 229 223 231 240Production (1000 tonnes)
Wheat production 181 164 307 242Barley production 237 369 597 603Canola production 11 19 40 58Fodder production – 28 55 318
Grain sold as% of grain produced – 2.2 7.5 14.2
LivestockNumbers (million)
Bovines 4.65 5.06 5.26 6.54Sheep and goats 18.25 16.81 16.64 15.79
Meat (1000 tonnes)Beef 20.9 43.5 84.8 169.1Mutton 24 39.3 56.6 81.4
Milk (1000 tonnes) 98.7 157.5 204 302.5
2.2. Previous studies
In the case of China, numerous studies have outlined the trans-formation of China’s rural sector. Various chapters in Longworth(1989) describe China’s ‘‘rural development miracle’’ followingthe economic and institutional reforms of the late 1970s. More re-cent papers such as Huang et al. (2010) describe the macro-factorsinfluencing Chinese agriculture and its rural sector. Less attentionhas been directed at western China, however, where the transfor-mation of the rural sector has not been as pronounced. Lu et al.(2004) explored various land use options with a focus on a seriesof ecological objectives for the Loess Plateau in Gansu using multi-ple-goal linear programming. Nolan et al. (2008) also examinedfarming systems in Gansu based on household surveys.
In the case of Tibet, household surveys have been used todescribe farming systems and discuss development options.Goldstein et al. (2003) drew on a large number of anthropologicaland socio-economic surveys of households to discuss developmentissues in rural Tibet including decollectivisation, economicdevelopment and labour migration, and population planning. TheMinistry of Agriculture also reported on issues and strategies forthe development of Tibetan agriculture. The study includedsurveys of a large number of farm households covering manyaspects including land use, labour, cropping, livestock, and assetsand finance to identify development issues and challenges withthe aggregate findings reported (in Chinese) in Fan (2007). Struc-tured interviews and small surveys have also been conducted inparticular areas. Zhang et al. (2008) surveyed households ineastern Tibet to explore livelihood diversification. Related to thisstudy, Paltridge et al. (2009) undertook a small preliminary surveyof agricultural households to build on the previously limitedinformation to provide a broad description of the farming systemsfor their examination of crop strategies.
This body of information provides useful information on thedevelopment of agriculture in western China and Tibet. Given the
86 C. Brown, S. Waldron / Agricultural Systems 115 (2013) 83–94
dynamic forces that have bought about change within these house-hold systems, however, additional insights can be gained throughthe modelling of farm households to explore different farming sys-tems and alternate policy, market and technological scenarios. Thisstudy draws on information contained in both the Paltridge et al.(2009) and Fan (2007) surveys. The modelling also requires veryspecific and detailed information on household and farming sys-tems, however, that requires in-depth case studies of farm house-holds to identify and synthesize the many model parameters. Themodel used in the study is outlined in the next section while thesource of data is presented in Section 4.
3. Model
To facilitate a detailed, micro-level analysis of the farming sys-tems and economics of the farm households, a simulation model ofagricultural households in Tibet was developed. An optimizationmodel drawing on the same data and exploring least cost feed op-tions and most profitable enterprise mixes under alternative mar-ket and production conditions has been developed in parallel to thesimulation model as a normative tool to help identify feed andenterprise scenarios worthy of investigation. The multiplicity ofhousehold objectives, complexity of the farming systems and needto identify detailed and disaggregated impacts across the farmingsystem, however, means that the primary focus has been on devel-oping a detailed, yet flexible, simulation model able to readily ex-plore and communicate the impacts of alternative scenarios,decisions, and market and production conditions.
Simulation models have been used to explore livestock-cropsystems elsewhere such as Lisson et al. (2010) for Bali cattle pro-duction in eastern Indonesia and Tittonell et al. (2009) for small-holder crop systems in Kenya. The model described below istailored to the features of Tibetan farms and agricultural house-holds. There is a strong emphasis on the bio-economic side of themodel with special attention to the valuation of all farm inputsand outputs, both monetary and non-monetary, as well as to thecash flow situation of the household.
The model is based on an annual steady-state cycle althoughtransitions to the steady state can be readily explored. The modelperforms physical and financial reconciliations—including on cashflow, labour, crop products, livestock feed, livestock numbers, live-stock products, manure and livestock energy and protein require-ments—and reports on various returns to household labour,management and capital. The basic time increment for calculationand reporting is monthly.
Among other key model parameters, the model endogenouslysimulates crop yields (based on land productivity, organic andinorganic fertilizer application and irrigation), livestock live-weights (based on livestock type, age, month), milk yields (basedon livestock type, liveweight, age and stage of lactation) and feedrations (based on ration type, nutritional content of feedstuffs,and energy and protein requirements). In addition, the model em-ploys a series of behavioural assumptions regarding householddecisions and resource utilization relating to labour, manure, feed,own consumption, land use and enterprise mix.
Several principles were instrumental in model design. First, aflexible model was needed to account for variations in agro-ecological conditions, household structures, locations and farmingsystems, and to accommodate wide-ranging policy and marketscenarios. The flexibility of the model is required both for use as a re-search tool and as a decision making aid for local extension officers.(Currently English and Chinese language versions of the model existwhile trials of the model with local extension officers have beenundertaken.) Second, intensive input requirements associated withmodel flexibility could compromise the capacity of the model to
capture the complexity of the household systems. Thus the modelhas been designed to facilitate ease of input while accommodatingdifferent levels of input sophistication. Key model- and data- inten-sive parameters such as feed rations, liveweights, crop yields andmilk yields are determined endogenously within the model but withprovision for users to override the values. Third, specific scientificknowledge, especially in respect of non-fertilizer crop yield re-sponses, livestock productivity and livestock feed requirements,are being rapidly discovered from a low base and so the model needsto be able to readily update technical parameters and response func-tions as trial and other data becomes available.
To facilitate its use as an extension tool, the model is run as aVisual Basic program embedded in a Microsoft Excel workbook.The following sections outline key parts of the model while someof the main physical flows including the external–internal house-hold linkages are highlighted in Fig. 2. A manual for the model thatprovides more specific details can be found in CAEG (2011).
3.1. Own consumption
Many agricultural products grown by households are self-consumed and so the model enables specification of own consumptionrequirements on either a monthly or annual basis or as a propor-tion of production. Cereals are used to make flour and produce beerwhile canola is pressed to make oil. Households normally out-source cereal grinding and oilseed pressing to specialized house-holds either in the village or local towns, so service processingcosts and ownership and use of by-products, such as canola meal,are accommodated in the model. Households combine straw withmanure for heating. In terms of livestock products, some fresh milkis consumed or used to make yoghurt, but most is used to producebutter and cheese. Households raise chickens to produce eggs forown consumption while wool and goat hair are used by house-holds to produce various fibre products for home handicrafts.Sheep, goats and pigs are consumed at festivals or over the winterperiod, although slaughtering is left to specialized slaughterhouseholds.
3.2. Land use
In Tibet, previous egalitarian land distribution systems meanhouseholds have numerous fragmented plots of different fertility.The model enables specification of different grades of land withassociated productivity indexes. Provision exists for renting land,which occurs in Tibet especially for fodder crops, but is not wide-spread for other crops. Crop land is located in and around river val-leys, but many agricultural households also graze ruminantlivestock on surrounding hills and river beds. The use rights on thisland vary across areas and over time as grazing pressures and pol-icy directions change, but most grazing occurs as either open ac-cess or common grazing within village areas. The modelaccommodates these scenarios through pasture availability, pro-ductivity indicators and use fees.
3.3. Cropping
Agricultural households grow a range of cereal, other food, fod-der and cash crops. The primary cereals grown are spring barley(planted April and harvested in October) and winter wheat(planted October and harvested in April) although winter barley,spring wheat and buck wheat are also grown. The cereals are usedfor own consumption (flour as well as beer making), livestock feedand for sale or exchange with other households.
Canola is another widely grown crop often just sufficient forown household oil needs but with some specialization as a cashcrop in areas targeted for industry development. Households also
Labour
Adult
Village
Senior/Child
Manure
Livestock feed
Localagricultural
producemarkets
Local meatmarkets
Village trade/barter
Own Consumption
Machinery
Off-farmemployment
Casuallabour
Fertiliser
Otheragricultural
inputs
External Internal
Non-dairy livestock productsGoat hairWoolEggs
Bovines Sheep & goats Pigs
Livestock turnoff
LandLand
Grade 1
RentalGrade 2 ...
Grazing
Joint productsButterFresh milk /
yoghurtCheese
Milk yield=f{type, age, stage oflactation, liveweight}
Dairy livestock products
Liveweight [LW]=f{Type, Age, Month}Livestock number (LN)=f{Type, Starting
numbers, mortalities, birth%, pregnancy%}
Livestock (by type & age)
Improved cattleLocal cattle Pigs
Yattle
Poultry
GoatsSheep
Livestock/meat traders
CropsYield=f{fert, water,other inputs}
Straw, husk & cropresidues
Root cropsFodder crops
CerealsOilseed crops
Pulses & legumesVegetables & other crops
Organicfertiliser
Brewerswaste
Oilseedmeal
CanolaoilBeer
Flour
Yaks
Cerealmarkets
Oilseedmarkets
Milking & butter/cheese making labour
Otherfeedstuffs
straw
oilseed meal
Foddermarkets
Heating
Heating
Slaughterhouse-holds
Option 1: Specify ration typeRation Type[RT]=f{livestock type, season}
Ration amount [RA]= f{RT, nutritional content offeeds, nutritional requirements of livestock, LN}
Feed supply [FS]FS=f {RT*RA*nutritional content of feedstuffs}
Feed demand [FD]FD=f{LN, nutritional requirements (ME,CP) of
livestock by type & age}
Energy (ME) / protein (CP)balance
Option 2: Specify ration amount with modeladjusting productivity
Option 3: Specify ration amount & livestockproductivity
Fig. 2. Overview of key physical flows in the model. Note: Solid lines represent internal household flows while dotted lines represent external flows.
C. Brown, S. Waldron / Agricultural Systems 115 (2013) 83–94 87
grow a range of vegetable and root crops for their own consump-tion (peas, potatoes and turnips) while some households havesmall gardens and greenhouses.
While Tibetan agricultural policy mandated the production ofcereals, this policy was relaxed when concerns about self sufficiencyin cereals abated at the end of the 1990s. This paved the way for anincrease in the production of fodder crops, including lucerne, maize,vetch and oats. Because of their relatively recent uptake, the preva-lence of forage crops varies by region and has strong project or pro-gram ties. Typically they are grown on lower productivity land withbetter land reserved for cereals and other food crops.
With the exception of lucerne, most food and fodder crops areannuals rather than perennials. The model also accommodatesmixed cropping, widespread in some areas notably for spring bar-ley and canola, as well as relay cropping, less prevalent but used forfodder crops such as legumes or turnips following a winter cereal.
For each crop, the model enables specification of various agro-nomic practices including land preparation, sowing, fertilizerapplication (both inorganic and organic), irrigation, weeding, pesti-cides, and harvesting. The timing (month) of the activity along withlabour use and costs are recorded to feed into the labour, returnsand other elements of the model. Crop residues—in particular
88 C. Brown, S. Waldron / Agricultural Systems 115 (2013) 83–94
cereal straw and husks—are specified via harvest index coefficientsbased on information from scientists from the Tibet AgriculturalResearch Institute. Storage losses are also recorded. The model usesinformation on these agronomic practices to feed into responsefunctions that generate crop yields although because of limitedagronomic trials these are currently only specified for fertilizerresponses.
Crop production can be used for own consumption, livestockfeed, and carryover seed while some may be stored. The modelsubtracts own consumption, livestock feed, carryover seed andstorage losses over a year from production with any surplus sold.Any shortfall of requirements over production is met through pur-chases. Although Tibetan farm households store cereals for longerthan 1 year, the value of sales acts as a proxy for the value of anychanges in crop inventory across years.
3.4. Livestock
Agricultural households in Tibet raise both intensively- andextensively-fed livestock. The intensively-fed or penned livestockinclude dairy cows, pigs and poultry. Most dairy cows are localcows although improved Holstein and Jersey crosses have emergedin recent years. The extensively-fed livestock include both largeand small ruminants depending on the region, which graze on sur-rounding hills and mountainsides as well as river beds and road-sides. Large ruminants include yaks as well as yattle (a yak-cattlehybrid). Yattle graze pastures relatively close to the householdand can be penned at night and for much of the winter. Converselyyak graze more remote pastures. Households in other agriculturalareas graze sheep and goats that are penned at night and for muchof the winter. The model currently specifies 29 livestock types (di-vided by species, sex age category and whether they are native orimproved breeds). Livestock numbers are reconciled based onstarting numbers, age, type, mortality, pregnancy and birth rates,and birth month. Liveweights are calculated endogenously withinthe model based on a reference liveweight, age and month of yearas significant weight losses occur over the extremely cold wintersdue to the higher maintenance energy requirements.
3.5. Livestock feed
Livestock feed lies at the core of integrated crop-livestock sys-tems in Tibet and of the model. The model reconciles feed demandwith feed supply. In respect of feed demand, the model determineslivestock numbers by type and month as described above. The met-abolisable energy and crude protein requirements for each live-stock type are calculated in the model based on type, liveweight,milk yields and stage of lactation/pregnancy. These unit energyand protein requirements are then multiplied by the relevant mod-el-determined livestock numbers to determine total feed demandor energy and protein requirements in each month.
The feed requirements or demand are met from a variety ofsources internal and external to the farm household. In total thereare 11 major types of feed specified in the model. These includefodder crops (lucerne, vetch, oats and maize) where the numberand timing of cuts determine the amount of feed available in anyparticular month. Fodder crops can be bought, sold or exchangedbut the markets are rudimentary and ration types often partlyalign with the fodder crop available. The dry season of 2010, how-ever, led to significant intra- and inter-regional trade of fodder and,with specialization of households and regions, this intra- and inter-regional trade can be expected to expand. Some households accesspastures and cutting land as a partial feed source for their rumi-nant livestock. The model distinguishes between grazing and cutgrass as there are different labour requirements, while cut grassand other green feed can also be purchased. Pasture yields vary
by month. There are also a range of crop residues, in particularstraw and husks, from the crops grown by the household. Stubbleis also grazed normally in a 4–6 week period after harvest. Thestraw is also used for household heating and so partly competeswith its use as a livestock feed.
Cereal grains comprise another major source of feed for Tibetanhouseholds. Cereals are often exchanged through barter betweenhouseholds, while the grains are often processed before being fedto livestock. Cereal grains for livestock feed compete with house-hold food consumption, while cereals are readily and widelytraded.
By-products from household own consumption form importantfeed sources, especially oilseed meal and brewers waste. Mosthouseholds reclaim the meal from the processing of their canolafor oil as a high protein feed source for their livestock. SimilarlyTibetan households use a significant quantity of barley (from100 kg to 300 kg per annum) to make beer for their own consump-tion. The brewers waste from the spent barley grains provides anadditional feed source. Both the oilseed meal, from householdpressers or from larger canola processing companies, as well asbrewers waste can be bought in.
Ultimately livestock feed demand needs to be reconciled withfeed supply and there are three alternative modes in the modelfor performing this reconciliation. In the first mode—used in themodel runs reported in Tables 3 and 4—users specify ration typeswhich represent proportion (by weights) of individual feedstuffsin a ration. The rations can be specified for different types of animalsand for different seasons (such as winter ration for yattle or springration for improved cows). Depending on the composition of the ra-tion and on the nutritional content of the feedstuffs, each kilogramof ration comprises a total metabolisable energy and crude protein.This is compared with livestock feed demand—as described aboveand dependent on livestock numbers and parameters such asliveweight and milk yields—to determine the total quantity of rationrequired to meet the total energy and protein requirements.
The second mode recognizes that households may feed theirlivestock on better planes of nutrition to achieve higher productiv-ity or, more commonly, underfeed their livestock resulting in lowerproductivity. The primary difference is that users now specify theamount of ration rather than the ration type, as in the case of thefirst mode, where the ration amount was determined endoge-nously in the model. The model calculates total energy and proteinfrom the ration fed and compares it with energy and proteinrequirements. Calculated deficits or surpluses between energyand protein availabilities and requirements are used to modifylivestock productivity parameters including liveweight gain, milkyields, egg production, fibre production, mortalities and birth andpregnancy rates.
The third mode recognizes that, in some cases, accurate infor-mation on the nutritional content of particular feedstuffs or thenutritional requirements of particular livestock may not be avail-able but that detailed information has been collected on rationsfed and livestock productivity attained. Thus in this mode, boththe amount of ration as well as livestock productivity are specifiedby the user rather than determined endogenously in the model.
Irrespective of the mode used, some of the feedstuffs aresourced within the household while some are sourced externally.The model reconciles livestock feed requirements against otherhousehold uses (such as carryover seed, own consumption andstorage losses for cereals or heating uses for straw) to determinewhat if any amounts of feed need to be purchased in.
3.6. Labour
Labour is another key household resource and component of themodel. Various categories of labour are specified in the model
C. Brown, S. Waldron / Agricultural Systems 115 (2013) 83–94 89
including adult and senior/child labour. Rules on the allocation ofcategories of labour to particular activities can be user-specified(such as senior labour used to look after livestock in summer onlyafter available adult labour has been used). Household labour isused for various activities including general household, crop, live-stock, feed and other activities as well as for off-farm activities andvillage labour commitments. (Village labour commitments can in-volve around 25 days of village work per household per year. Thework is co-ordinated and at the discretion of the village leader tak-ing into account the resources and labour availability of house-holds and common village tasks required such as road orirrigation maintenance or construction, operation of irrigationditches, common pesticide spraying.) Labour requirements forspecific farm or household activities (such as butter and cheesemaking) are based on labour coefficients as well as parametersdetermined elsewhere in the model (such as milk yields and live-stock numbers). Labour requirements, including those off-farm,are reconciled with labour availabilities taking account of any con-straints on use of labour by type to identify any labour surplus ordeficit which is then met in the model through casual labour.
3.7. Livestock products
The small number of livestock raised by households means thatmost livestock products are consumed by household membersthemselves (own consumption). This applies to wool, goat andyak hair although some fibre is used to produce handicrafts as asideline business. Households usually keep only enough layersfor own egg requirements although Tibetan eggs are a specialtyproduct in high demand in urban areas in Tibet and elsewhere inChina. Some livestock are sold to dealers for slaughter or for ownconsumption but often on an irregular basis. Valuation of changesin livestock inventory (through natural increase and decreases) arecritical in the model in determining returns as outlined below.
Milk is one of the most important livestock products. Tibetanhouseholds traditionally use a variety of dairy products in foodconsumption while milking, butter- and cheese- making, and feed-ing dairy cows account for a significant proportion of household la-bour. Households consume relatively little fresh milk althoughsome is used to make yoghurt on an irregular basis. Instead, mostmilk goes to producing butter with a ricotta style cheese as a jointor by-product.
Another livestock product straddling several parts of the house-hold system is manure. Manure is mixed with straw for use inhousehold heating. It is also an organic fertilizer sometimes as asupplement to inorganic fertilizer or sometimes as the sole fertil-izer source. In the model, manure is first used to meet householdheating requirements with any remaining manure balance thenavailable for fertilizer application. If there is insufficient manureto meet user-specified application rates, the rates are diminishedto the amount available with crop yields modified in line withthe crop response functions for organic fertilizer.
3.8. Subsidies and finances
Various transfer payments and subsidies are available to Tibe-tan farm households including fertilizer, machinery and land sub-sidies as well as housing and welfare payments. While subsidies donot form outputs from the farming systems, they do impact onhousehold cash flow and do figure in household perceptions andincentives to engage in particular activities. Partly as a result ofthese subsidies and other programs, the level of mechanizationon farms has expanded significantly since 2000 while livestockpens and sheds have increased. Although these are partly subsi-dized, depreciation and repairs and maintenance on these assets
have become significant items and so are incorporated explicitlyin the model.
3.9. Economic returns
Apart from the bio-physical reconciliations calculated and re-ported in the model, the model also calculates and reports both aprofit statement and a cash flow statement. The profit statementis used to determine the viability of farming systems and theincentives for households to pursue particular farming activities,while the cash flow statement determines its feasibility. Both high-light the importance of agricultural income to households relativeto other sources of income.
The profit statement represents the value of all farm outputsminus the value of all inputs used, irrespective of whether thesevalues are monetary or non-monetary. Indeed, the value of ownconsumption and changes in the value of livestock inventoriesare of most importance on the revenue side for these semi-subsistence farms (see Table 3) while depreciation is a significantnon-monetary expense especially as farms become more mecha-nized and accommodate more specialized intensive livestock sys-tems. The profit statement also highlights the value ofintermediate inputs, the most important of which is livestock feed,but which also includes carryover seed and manure used for fertil-izer. Reporting the value of livestock feed grown and used by thehousehold is important because its value can be overlooked byhouseholds and extension agents thus underscoring the contribu-tion of fodder crops and self-produced feedstuffs to household re-turns and overstating livestock profits.
Because households engage in different off-farm activities andvalue their own labour in various ways, a series of economic re-turns (rather than just net farm income) are calculated to reflectthe different incentives to households. Besides net farm income,the opportunity costs of labour and capital are subtracted to deter-mine a return to management from farm activities. The valuationof labour exerts a major impact on household returns.. Returns tomanagement from both farm and non-farm activities are also cal-culated and are of increasing relevance to households as they de-velop a portfolio of on- and off-farm activities. As mentioned,subsidies only represent transfer payments rather than real out-puts. Nonetheless households align receipt of the subsidies toundertaking farm activities and so these transfer payments are alsoreported.
The other economic return presented is a detailed monthlycash flow statement. The statement records all cash flowing inand out of the household whether they are related to farm activ-ities or not. Thus it includes items such as off-farm income andmedical expenses but excludes non-cash items such as valuationof own consumption or depreciation. By highlighting potentialmonthly cash deficits or surpluses, the statement indicates feasi-bility by identifying the cash required to implement farm plans inthe context of other household activities. Furthermore, given theagrarian transition in Tibet where households are required topay more for health, higher education and consumer products,while also having more cash generating opportunities, the extentto which the farming system generates cash flows to meet house-hold cash needs can be as important as the profitability of thefarming system.
4. Data
Accommodating flexibility and accounting for the complexity ofthe household systems in the model requires a large amount ofspecific information. As mentioned, the primary source of this de-tailed information came from farm case studies in three agricul-
90 C. Brown, S. Waldron / Agricultural Systems 115 (2013) 83–94
tural regions in Tibet: Bailang county in Shigatse prefecture; Nai-dong County in Shannan Prefecture; and Mozhugongka County inLhasa Prefecture. The location of these areas is shown in Fig. 3while key county statistics are presented in Table 2. Detailed inter-views were conducted with households on land use, finances, ownconsumption, cropping systems, livestock systems, feed systems,prices, off-farm activities, labour and household structures. Twentyhousehold interviews were conducted in 2009. Most householdswere re-visited in 2010 to fill information gaps, to check on themodel’s robustness, to gain a better appreciation of the dynamicsof these systems, and to understand adaptation of these farmhouseholds, especially as 2010 was a very dry year with impactsnot only on crop yields but also on price and availability of feedresources.
The case study households were selected in consultation withregional extension officers from the Tibet Academy of Agriculturaland Animal Sciences as well as local officials. The households camefrom at least two villages in each of the counties while they werealso selected to account for a diversity in size (household numbers,crop areas and livestock numbers) and incomes. Village level dataon the distribution of households by size and income classes in Bai-lang and Naidong counties was obtained to place the case studyfarms in context and to aid interpretation of model results. Localextension officers participated in the household interviews andprovided additional information on model parameters, and high-lighted the representativeness of the interviewed household. Thecase study interviews were used not to provide average valuesfor particular parameters but to synthesize these parameters basedon the case study interviews as well as other information sourcesincluding previous surveys. Secondary information collated fromregional statistical yearbooks were also used to place the specifichousehold information in a broader context but were of limiteduse because of underlying biases in the data and masking associ-ated with the level of aggregation.
Specific questionnaires designed to fill the information gaps inthe model were prepared. The order and framing of the questionswas then modified by a Tibetan collaborator experienced andskilled in interviewing Tibetan farm households. Further refine-ments to the questionnaire were made after the initial interviews.The interviews were constructed as semi-structured interviewsthat facilitated the collection of core information across house-holds but that also allowed extension of questions into areas wherethe particular household had good information. The interviewsgenerally involved half a day while several members of the house-hold were interviewed to obtain information on particular items.
Apart from assistance with the selection of households, localofficials were also interviewed to gain an understanding of histor-ical developments and local strategies and policy directions. Enter-prise managers (including grain and dairy processors and feedcompanies) along with local traders were interviewed to gatherinformation on local markets, prices, product requirements andservice costs over a period of years.
The model requires a range of technical parameters from cropyields and responses, livestock productivity, livestock feed require-ments and feed nutritional contents. Information was obtainedfrom agronomists and animal production and nutrition scientistsfrom Tibet Agricultural Research Institute (TARI) and Tibet Live-stock Research Institute (TLRI) of the Tibet Academy of Agricultureand Animal Sciences as well as overseas scientists involved in col-laborative research with these institutes (see, for instance, Wilkinsand Piltz, 2008). Information from previous research and surveyssuch as that in Paltridge et al. (2009) was also drawn upon whilegeneral information on feed nutritional contents and energy andprotein requirements was drawn from sources such as NRC(2001). However, as Paltridge et al. (2009) noted there is a signifi-cant difference between potential and actual crop yields in Tibet
while lactation curves, livestock productivity and feed nutritionalcontents are also region specific. Thus many of the technicalparameters such as crop and milk yields were based heavily oninformation collected in the case studies. The TARI and TLRI scien-tists and extension officers, with their extensive local knowledge,provided a useful reference point for commenting on the informa-tion from the case studies and in establishing farm level parame-ters. As mentioned in Section 4, the model simulates a range ofparameters such as crop yields, liveweights, milk yields and feedrations but the model allows the user to overwrite these endoge-nously determined values to account for individual variation.
In examining the shift from staple food crops and local livestockbreeds to improved breeds and fodder crops in this paper, it isimportant to note that some of the fodder crops and improved live-stock breeds are a relatively recent introduction. Although thereare active research programs at TARI and TLRI in these areas (see,for instance, Heath et al., 2012; Wilkins and Piltz, 2008), they arestill at a preliminary stage. Thus it was important in the modeldesign to be able to incorporate new and robust information fromfertilizer, dairy and feed trials as and when that information be-comes available. For the analysis, care needed to be exercised toensure that prices in these areas (such as for fodder seed) reflectedlonger term prices instead of artificially inflated prices associatedwith early stages of industry development.
5. Results
5.1. Base analysis
The model outlined in Section 3 can reveal important character-istics about the farming and household systems for different typesof agricultural households in Tibet. The middle columns of Table 3report key summary results for household types from the threecase study areas. Mozhugongka households are sufficiently far en-ough away from Lhasa not to be considered as peri-urban, but arenevertheless involved in significant off-farm work, especially fun-gus-picking, while the livestock (local cows) and cropping systemsare less intensive. Naidong households have less labour and house-holds members reliant on the agricultural systems (as some mem-bers have full time employment off-farm). Compared with theMozhugongka households, they tend to raise improved rather thanlocal cows while the small household labour sees them less in-volved in other grazing livestock. Relative to Mozhugongka andNaidong, Bailing has larger household and land sizes, livestockand cropping systems are more intensive, and off-farm work lessprominent than in Mozhugongka. Table 3 reveals the differencesthat exist between the household types, but also highlights somecommon aspects. Although these cases are representative of manyhouseholds throughout agricultural areas in Tibet, agriculturalhouseholds can still be very diverse in Tibet (such as povertyhouseholds with little household labour, farm land or capitalresources) and hence the need for a flexible model such as thatdescribed in Section 3 to accommodate the different householdtypes.
For the three household types, around half of the value of out-puts is for own consumption with only one-fifth to one-eighth ofthe value of outputs generated by commercial sales. Despite live-stock feed purchases accounting for around one-sixth of householdcash costs, they represent only a minor proportion of the value ofall feed fed to livestock with over 80% sourced as an intermediateproduct within these farming systems.
Returns to management from farm activities are modest rang-ing from Rmb2674 for Mozhugongka to Rmb5432 for Bailang.However, the returns depend crucially on the opportunity cost orvaluation of labour used to generate the farm outputs. For instance,
C. Brown, S. Waldron / Agricultural Systems 115 (2013) 83–94 91
in Table 3 labour is valued each month at an opportunity cost thatreflects alternative off-farm opportunities, which are plentiful insummer and few in winter. Valuing the labour at casual labourrates reduces the return to management from farm activities forthe Bailang case from Rmb5432 to Rmb-1243, while a zero valua-tion on labour (often perceived by households) increases returns toRmb17,357. Most households respond to total net returns (net re-turns to their own labour, capital and management and includingany off-farm income, taxes and other transfer payments or subsi-dies), however, and these are highlighted in Table 3 ranging fromRmb16,503 for Naidong to Rmb28,240 for Bailang.
In terms of feasibility, the cash flow needed to generate thefarm outputs is modest at less than Rmb2600. One of the primarydifferences between the household types relates to the relativeimportance of off-farm income. For Mozhugongka, off-farmincomes represent 80% of the equivalent farm returns, namelythe returns to management and labour for farm activities, whilefor Naidong it is 48% and 34% for Bailang. In general, net cash salesfrom agricultural products are sufficient to cover agriculturalexpenses and incidental household expenses but insufficient tomeet large, irregular cash expenses incurred by households (suchas unanticipated medical expenses, education or job expenses,house renovations and furnishings) which typically are met fromoff-farm income.
A key component of these household systems is labour which isfully utilized during the summer months when both off-farm activ-ities and cropping and livestock activities are at their peak. Con-versely a large surplus of household labour occurs in winter(between 123 and 168 person days from November to March)when many farm and off-farm activities close down. Livestock la-bour accounts for over one-quarter of total labour use in the busysummer period while labour intensive butter and cheese makingand milking account for over half of this livestock labour.
Cereal grain yields are sufficient to meet own consumption ofcereals for food and beer making but insufficient to meet bothhuman food and livestock feed requirements in Bailang andMozhugongka with 8–22% of feed requirements bought in. Canolais another widely grown crop primarily used by the household fortheir own cooking oil requirements but also to generate modest sales.
Most of the crops—both cereal and fodder crops as well as byproducts including straw, husks, canola meal, and beer makingwastes—are used as an intermediate input to household livestockproduction with a value of Rmb7016 in Naidong and Rmb20,640on the larger crop and livestock farms of Bailang. Nevertheless,even with the relatively small livestock numbers and utilization
Fig. 3. Map of s
of feed sourced within the household including common accessgrasslands, significant quantities of feed are bought in.
The main livestock products for these households are butter andcheese but the livestock also supply eggs, fibre and meat. Changesin livestock inventory through new calves, lambs, kids and pigletsaccount for a significant part of the livestock value with increasesin the value of bovine inventories being the largest component. Thescale of livestock production means that most of the products areused for own consumption with, for instance, net butter sales rang-ing from only 6–12% of production. Manure is an integral part ofthe household system both for household heating and organic fer-tilizer. For these households, manure production is sufficient forheating but for the Bailing farms is insufficient for the desired or-ganic fertilizer application rates on these larger cropping areas.
5.2. Specialization and sensitivity analysis
As discussed in Section 2, agrarian change and governmentpolicies place pressure and provide incentives for households tointensify production, become more integrated into markets andto specialize in particular activities. The model enables the impactson households of intensification, integration and specialization tobe explored, and one particular specialization pathway promotedby government and research agencies is examined in this sectionfor illustrative purposes, namely scaling up and specialization indairy production and an associated shift from food to fodder cropsin Bailang. Specifically the number of cows doubles from 4 to 8with a shift out of small ruminants and pigs. Some 4 mu of landpreviously mixed cropped with barley and canola and 4 mu of sin-gle cropped barley is replaced with 8 mu of oats. (One mu isapproximately one-fifteenth of a hectare.) Key summary resultsfor the specialization runs appear in the right hand column ofTable 3 and can be compared with the base results in the columnto its immediate left.
Increasing dairy and fodder production increases returns tomanagement from farm activities from Rmb5432 to Rmb11,921.The increased returns are associated with much greater exposureto external markets. Households switch from being subsistenceto ‘‘commercial’’ dairy producers having to sell more than half oftheir dairy products and with dairy sales accounting for over halfof all cash receipts. Conversely, off-farm income as a percentageof cash receipts falls from 37% to 23% despite the same amountof off-farm work being undertaken. Casual labour is needed onlyfrom May to July when off-farm work is being undertaken despitethe extra labour required for milking and butter and cheese
tudy areas.
92 C. Brown, S. Waldron / Agricultural Systems 115 (2013) 83–94
making. Although the scenario involves specialization into foddercrops and despite the larger crop areas than in some other agricul-tural areas in Tibet, own feed accounts for only 71% of the value ofall feed fed to livestock compared with 86% in the base case, whilefeed purchases increase to 26% of cash costs as households have tobuy in large amounts of straw, cereals, oilseed meal and brewerswaste to feed the extra cows. Thus specialization in the productionand exchange of straw and other feeds across households is neededat a local level. For some intensive cropping areas, the trade of feedbetween households without large net imports into the area maybe realistic provided all households do not scale up dairy produc-tion to the same extent. In some of the less intensive and crop landconstrained areas, however, large amounts of feed may need to bebought into the region to feed an expanded dairy herd. (See
Table 3Summary results for the Mozhungongka, Naidong and Bailang household types as well as
Profit/net returnsGross Farm Revenue (Rmb)� Own consumption as% of total revenues� Sales as% of total revenues� Inventory (livestock & feed) change as% of total revenues
Gross Farm Expenses (Rmb)Value livestock feed from own production (Rmb)� Own feed as% of value of all feed fed to livestock
Return to management for farm activities [RMFA] (Rmb)� Opportunity cost of household labour (Rmb)� Opportunity cost of labour for farm activities (Rmb)� RMFA with labour valued at casual labour rates (Rmb)� RMFA per full time equivalent labour available (Rmb)
Return to management, labour and capital [including taxes, off-farm income andsubsidies] (Rmb)
Off-farm income as % of returns to management and labour for farm activities
Cash flowMinimum monthly cash flow balance (Rmb)Off-farm income – Rmb (% of cash receipts)Net dairy product sales as % of cash receipts (%)Feed purchases as % of cash costs (%)
LabourOff-farm labour (person days)Casual labour required (person days)Adult surplus labour – November to March (person days)Crop labour as % total labour in April (%)Livestock labour as % of total labour in June (%)Livestock labour as % of total labour in December (%)
Cropping/land useCrop areas (mu) a
Cereal production in kg (sales as % of production)Oilseed production in kg (sales as % of production)
FeedStraw required in kg (% bought in)Cereal required in kg (% bought in)Oilseed meal and brewer’s waste required in kg (% bought in)Lucerne, vetch and oats required in kg (% bought in)Cut grass and pasture in kg fresh weight (% bought in)
Livestock and livestock productsLivestock year start numbersb
Milk production (litres)Net butter sales as % of productionChange in value of livestock inventory (Rmb) of which:� Bovine inventories (Rmb)
Increase in calves (number)
ManureManure production (kg) of which:� Used for heating (kg)� Used for organic fertilzer (kg)
a Areas are in mu where 1 mu equals one-fifteenth of a hectare. ‘‘WW’’ refers to winterto oats’’; ‘‘V’’ to vetch; ‘‘P’’ to pastures.
b ‘‘IC’’ refers to improved cows; LC’’ to local cows; ‘‘D’’ to draught bull; ‘‘Y’’ to yattle;
Komarek et al. (2012) for a model that investigates local level,aggregate feed constraints in the case of goat-lucerne systems inagricultural areas of Gansu province.)
Table 4 reports returns to management for farm activities for allthree household types and the specialization case under alterna-tive market and production scenarios. Given the importance offarm activities to these households as outlined in Section 5.1, onemain purpose of the sensitivity analysis was to explore potentialdownside risks to these farm returns. Scenarios 2, 3 and 4 in Table 4involve substantial market and production changes but, nonethe-less, are within the bounds of previous adverse movements inmarket and production conditions. The results highlight thatspecialized households, in general and as expected, are impactedmore by the movement in product prices and labour rates.
the Bailang specialization scenario.
Mozhugongka Naidong Bailang Bailang specialization
19,626 17,163 28,973 38,79347 59 56 3817 21 13 3736 20 32 258422 8381 11,009 14,54112,670 7016 20,640 13,97681 78 86 712674 3192 5432 11,92114,462 8873 16,665 16,6738262 5273 11,925 11,723�2607 �828 �1243 57511824 1005 1552 340623,601 16,503 28,240 33,936
80 48 34 23
�244 �2566 �2394 �21608850 (50) 4050 (31) 5850 (37) 5850 (23)8 15 16 5418 13 18 26
270 135 180 18055 33 32 53139 123 168 20114 25 38 3227 26 31 3568 50 72 71
11SB, 3C, 2V,100P
10SB, 1C,4L
4WW, 8SB, 8MBC, 5O,45P
4WW, 4SB, 4MBC, 13O,45P
3249 (0) 3000 (21) 5456 (0) 3224 (0)360 (54) 99 (0) 360 (37) 180 (0)
6216 (34) 4015 (0) 7039 (0) 11,609 (60)3570 (22) 2221 (0) 5170 (8) 3919 (30)156 (48) 161 (56) 627 (35) 446 (76)243 (0) 964 (29) 2560 (13) 6251 (1)25,472 (0) 0 (0) 32,279 (10) 0 (0)
3LC, 1D, 5Y, 1P,15L
3IC, 3L 4IC, 1D, 20S6G, 2P, 8L 8IC, 8L
3284 3114 4152 830412 7 6 537003 3610 8903 96265702 3612 4816 96322.5 2.2 2.9 5.8
6082 3782 10,175 10,0862325 2325 2325 23252200 1457 5000 5000
wheat; ‘‘SB’’ to spring barley; ‘‘MBC’’ to mixed barley and canola; ‘‘C’’ to canola; ‘‘O’’
‘‘P’’ to pigs (sows); ‘‘L’’ to egg layers; ‘‘S’’ to sheep; ‘‘G’’ to goats.
Table 4Sensitivity analysis.
Mozhugongka Naidong Returns to management for farm activities (Rmb)Bailang
Base case Specialization case
1. Base scenarioa 2674 3192 5432 11,9212. Lower dairy price scenario 1196 895 2363 57973. Dry year scenario �850 �101 �671 62564. Higher wage scenario 883 1624 3089 94845. Yield improvement scenario 4259 7620 8481 15,6046. Higher price scenario 5512 5527 9843 17,843
a Scenario 1 refers to the base models and results presented in Table 3; Scenario 2 decreases dairy product prices by 25%; Scenario 3 decrease crop yields by 25% with localfeed prices rising by 25% and the prices for crops partly integrated with broader national markets (such as cereals, canola) increasing by 10%; Scenario 4 increases casual andoff-farm wage rates by 25% (except for fungus picking) with corresponding adjustments to labour opportunity costs; Scenario 5 increase crop and milk yields by 10%; andScenario 6 increase all prices by 20%.
C. Brown, S. Waldron / Agricultural Systems 115 (2013) 83–94 93
However the specialized households still outperform the basehouseholds at least in terms of returns to management from farmactivities. Falling dairy prices dramatically reduce returns as thespecialization involves scaling up to 8 cows. However dairy priceswould have to fall by more than 52% for negative returns to man-agement from farm activities for this specialized household. Thethird scenario reflects the dry 2010 season where yields were low-er and local feed prices higher and highlights the substantial im-pact on household incomes. For food crops, the reduced yield ispartly offset by the corresponding rise in price. As much of the cropproduction is for livestock feed, however, there is a dual effect ofdeclining feed availability, and so the need to purchase a greaterproportion of feed, as well as increased prices on the feed boughtin. The higher wage scenario (Scenario 4) impacts through the sig-nificant extra casual labour needed to service the extra cows, butthe impact is not as great as the lower dairy price or dry yearscenario.
The final two scenarios in Table 4 reflect possible areas forimprovement in farm returns. Scenario 5 examines the case ofhigher crop and milk yields that might be associated with the addi-tional investment on research and development on Tibetan agri-culture, while Scenario 6 increases agricultural prices to reflectthe increasing trend in grain, meat and other agricultural pricesin China since 2008. The results in Table 4 highlight that both thesescenarios markedly increase returns to management for farmactivities across all of the household types. In general, the higherprice scenario has a more pronounced impact than the yieldimprovement scenario.
6. Implications and concluding comments
Section 5 highlighted that existing farming systems are feasibleand align with household resources, but are only marginallyprofitable in terms of any increase in net worth to the householdafter valuation of all inputs and outputs. However, the profitabilitydepends critically on the valuation of own household labour. Issuesassociated with the valuation and utilization of labour have majorimplications for household incomes, livelihoods and developmentoptions. The analysis highlighted the very seasonal pattern ofhousehold labour use with some casual labour required in summerperiods and significant excess household labour during winter. Themain off-farm income opportunities are construction—concentratedin summer and restricted in winter when construction ceases—andfungus picking—again restricted to summer months. Intensifyingagricultural production exacerbates the labour situation, especiallyscaling up livestock. Thus the development conundrum is thatseeking to increase household incomes either through greateroff-farm employment or through intensification of existingcrop and livestock activities comes up against tight householdlabour constraints. Furthermore it fails to take advantage of
under-utilized household labour during winter. Off-farm opportu-nities outside of the peak periods and farming systems with a moreeven distribution of labour requirements will have importantdevelopment benefits. Identifying and realizing these opportuni-ties and systems given the extreme winter conditions on theplateau, however, is a major challenge.
The findings also indicate that farm activities are primarilyaimed at feeding the household rather than generating cash in-come. Only 13–21% of the value of farm outputs were commercialsales. Various development implications follow. Cash expendituresfor both essential and non-essential goods and services are likely toincrease in the future, and need to be met through either more sub-stantial off-farm opportunities and/or more intensive, specializedand commercially-oriented farming and household systems. Thiswill significantly alter the nature of the risks faced by householdsas well as the opportunities available to them. Greater farm sur-pluses, off-farm sales and specialization will also put pressure onthe very localized (often intra- and inter-village) market channels.Thus a range of policy considerations and settings arise fromensuring households are able to cope with the new risks throughto improving market channels and engagement of smallholdersin these markets.
In terms of identifying and implementing new crop and live-stock systems, two main implications follow. First, the analysishighlights how specific crop and livestock changes impact acrossall household activities. The semi-subsistent farming systems haveevolved in line with household resources. Increasing the number ofdairy cows will have implications not only for household labour—as butter and cheese making is a labour intensive process—but alsofor feed—with a need to buy in more feed as requirements extendbeyond own feed resource capabilities—and market engagement—as milk/butter/cheese production exceeds own consumptionneeds. Second, if the intensification and expansion is village or re-gion wide, this will impact on the type, amount and price of feedsavailable, as well as on the capacity of the local market to absorbthe increase in supply of product and associated impacts on pricesand need for engagement in external markets.
For better or worse and for the reasons outlined in Section 2,agrarian change is occurring in rural areas of Tibet as it is in otherparts of western China. Thus an awareness of the opportunitiesand challenges this presents to farm households is crucial. Theanalysis in Section 5 highlights that it is possible to formulatespecialization paths and changes to farming and household systemsthat increase net returns. However households will weigh theincrease in returns against increasing risk exposure and depen-dence on external markets with implications for adoption anduptake. The change in farm practices will need to demonstratesubstantial increase in returns before being considered. Thisincreases the demand for more commercially-oriented institutionalsystems, including well-functioning credit and insurance markets,
94 C. Brown, S. Waldron / Agricultural Systems 115 (2013) 83–94
inter-regional product and feed markets, and adequate safety netprovisions.
Given the demographic and bio-physical constraints on farmhousehold systems in Tibet and in many other parts of westernChina, it is unlikely that agricultural interventions will increaserural household incomes to those in urban areas and in othersectors. Thus the search to increased rural incomes will need toextend beyond specialization and intensification on-farm. Migra-tion and off-farm employment facilitated by ongoing agrarianchange may offer a path in the medium to longer term, but atten-tion to improving rudimentary and often dysfunctional marketingsystems, especially those for specialty products, warrants closeconsideration. In Tibet, better functioning markets for Tibetan eggs,yak milk and other unique products from the region could generatesizeable benefits for the small, low income, households. Brownet al. (2011) analyze specialty product markets in western Chinaas they relate to household livelihoods.
Acknowledgements
The authors would like to acknowledge the contributions of theproject scientists on ACIAR (Australian Centre for InternationalAgricultural Research) project LPS/2006/119 entitled ‘‘Integratedcrop and dairy systems in Tibet Autonomous Region, PR China’’on which the research for this paper was based and especially,but in no particular order, Nyima Tashi, Se Zhu, Jin Tao, Tsam Yu,Bemba Drolma, Pubu Drolma, Tim Heath, John Wilkins, AnnMcNeill, John Piltz, Nick Paltridge, Nicole Spiegel, David Coventry,Jay Cummins and Carol Rose. In addition, the authors would liketo thank and acknowledge the many households, officials, enter-prise managers and others involved in the study, as well as theinsightful comments of three anonymous referees. Of course anyomissions or errors in the paper are the sole responsibility of theauthors. The authors also gratefully acknowledge the financialsupport of ACIAR in funding the research.
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