A quantitative approach for assessing the productive performance and ecological contributions of smallholder farms

Agricultural

PII:

SO308-521X(97)00022-X

Systems, Vol. 55, No. 4, pp. 503-533, 1991 f> 1997 Published by Elsevier Science Ltd All rights reserved. P&ted in Great Britain 0308-521X/97 $17.00+0.00

tLSEVlER

A Quantitative Approach for Assessing the Productive Performance and Ecological Contributions of Smallholder Farms J. P. T. Dalsgaard* International

& R. T. Oficial

Center for Living Aquatic Resources Management MC PO Box 2631, 0718 Makati City. Philippines (Received

20 May 1996; accepted

20 January

(ICLARM),

1997)

ABSTRACT A pragmatic framework for monitoring, modelling, analysing, and comparing the state and performance of integrated agroecosystems is proposed. Four smallholder rice farms (I .36-2.76 hectares) - including monoculture rice and diverse rice-based systems with livestock, aquaculture, tree, and vegetable components - are used to illustrate the approach. Data collection employs a range of techniques (bioresource,floi+, diagramming, farm transects, direct observation, field measurements, .farm records, and informal discussions). Weekly data gatherings are used to construct annual, massbalunce, nutrient (nitrogen) flow models of each .farm system. The models form the basis for quanttfying a series of ugroecologicai system attributes (species richness, agricultural diversity,, eficiency, harvest index, productivity, nutrient cycling, throughput, standing biomass, production/biomass, biomass/throughput, and ugroecos_ystem nutrient balance) and economic properties (gross margin and returns to labour) for each ,farm. The comparative anul_ysis suggests that ,i+rat w perceive as ecologically sound farming, i.e. diverse and integrated natural resources management, can indeed be productive, profitable, and munageuhle, given uccess to labour and secure tenure. This type of unalvticul framework can help operationa!ise the sustuinability concept. 6 1997 Published hv Elsevier Science Ltd

INTRODUCTION Widespread Iand degradation under both intensive agriculture on fertile lands and under more extensive forms of agriculture in marginal environments *To whom correspondence Research Centre Foulum,

should be addressed at: Danish Institute PO Box 50, DK-8830 Tjele, Denmark. 503

of Agricultural

Sciences,

504

J. P. T. Dalsgaard,

R. T. OJicial

suggests: (1) a general need for alternative farming strategies that manage natural resources in an efficient, ecologically sound, yet productive manner; (2) a need for research methods that describe (qualitatively) and assess (quantitatively) the productive performance and ecological impact of complex agricultural scenarios. Here we outline an approach to model, describe, analyse, and quantify the productive and ecological characteristics of agroecological systems at the smallholder farm level. In the broad sense, agricultural systems (Spedding, 1979) or agroecosystems (Loucks, 1977; Douglass, 1984; Conway, 1985) encompass social, economic, geographic, and biological aspects of farming. In the narrow sense adopted here, agroecological systems analysis emphasises the ecological dimension of farms (Lowrance et al., 1984; Gliessman, 1990; Altieri, 1995). Despite the predominance of a particular crop or animal, tropical smallholder farms are very often characterised by the cultivation and utilisation of a wide range of species (Beets, 1990). These are sometimes farmed in combination, sometimes managed as separate entities - sometimes with the sole aim of producing a saleable or consumable output, other times with the additional purpose of servicing and maintaining other parts of the agroecosystem. This gives rise to a number of issues. Firstly, ignoring agronomic diversity and judging a farm’s productive performance on the basis of main crop yield alone may not adequately capture the productive state of the agroecological system. Secondly, judging the value of a component solely on the basis of primary (harvested) yield may underestimate its real contribution to the performance of the agroecosystem. And thirdly, ignoring agroecosystern complexity leads to inadequate qualitative understanding and quantitative assessments of the ecological impact of farming. Without due attention to the ecological complexity and soundness of farming, however, we are unlikely to be successful in attempts to generate sustainable agriculture. Integrated natural resources management Recent reviews of experiences from the field (Reijntjes et al., 1992; Pretty, 1995) have reinforced the notion that insights into sustainable agriculture can be gained from studying farms and working with farmers in the field. It is now commonly acknowledged that complementary use of resources in mixed cropping or agroforestry, as widely practised in smallholder farming, can lead to ‘overyielding’: grown in mixed stands, two or more crops and/or trees produce a higher yield than if cultivated separately (Willey, 1981; Cox, 1984; Francis, 1986; Young, 1989). The potential advantages of ‘mixed culture’ are also well known within aquaculture: by combining rice and fish (dela Cruz et al., 1992) or by stocking several species of fish together in a

A quantitative approach for assessing smallholder farms

505

polyculture pond, each occupying a separate food niche, more efficient and higher-yielding systems can emerge (Little and Muir, 1987). Javanese homegardens, with annual and perennial plants combined, frequently produce a higher net income than do adjacent monoculture rice fields (Soemarwoto and Conway, 199 1). Yuan and Leng (1993) compare semi-intensive traditional Chinese crop-hog-fish agroecosystems with conventional heavy input systems and find that the former perform better in both ecological and economic terms. Gliessman et al. (1981) judge the traditional diverse agroecosystems of the southeastern Mexican lowlands, the so-called chinampas, to be in many ways superior to more modern, less diverse systems. Referring to the century old Chinese polyculture systems, both Guo and Bradshaw (1993) and Ruddle and Zhong (1983) show how well balanced and carefully integrated aquaculture-agriculture systems provide the basis for very efficient nutrient recycling and an extremely intensive and productive, yet environmentally benign, production system, Harris (1996) gives a detailed account of a successful, intensive, productive and sustainable semiarid farming system, based on well balanced crop-livestock integration in a densely populated area. Irrespective of the environmental setting, agriculture thus seems able to benefit from integrated natural resource management. The potential synergisms of integrated farming are easily described through observations and anecdotal accounts. They are often far more difficult to demonstrate in quantitative terms. Whereas the potential advantages of mixed cropping can quite comfortably be researched and demonstrated through station and onfarm trials, more complex systems of crops, livestock, and perennial plants, are difficult to mimic and test under controlled conditions. The alternative is to study these systems as they already exist, in situ.

METHODS Four Philippine smallholder rice farms were identified for the study. Resource constraints (financial, time, and human) prevented the selection of a larger sample and replicates of farm types, and limited the period of study to one annual management cycle. The method and analysis presented should thus be seen as exploratory. The timeframe adopted provides a detailed snapshot of the current state of each system. Ideally, complex farms should be monitored and assessed over a period of several (e.g. 3 to 5) years in order to arrive at a more robust and coherent picture of their state and performance. The seasonal and annual achievements are subject to the fluctuations and vagaries of the market, climate, and other forcing functions. Longerterm quantitative on-farm studies of complex systems are still rare, however,

J. P. T. Dulsgaard, R. T. Ojicial

506

probably because they are demanding in terms of both donor, researcher, and farmer commitments. Farm types and selection The farms were identified through visits, farm walkabouts, and discussions with households on past, present, and future management plans. The four farms are located within two neighbouring communities, on Entric gleysols (FAO), predominantly clay and clay loam. The climate is humid tropical, with a distinct wet (June-November) and dry (December-May) season and an average annual rainfall close to 2000mm. The altitude is lOO-150m and the area classified as irrigated lowland rice farming. The four smallholder systems were selected along a horizontal axis spanning from the chemical-intensive monoculture rice farm to the diverse and integrated rice-based system. The approximate relative positions of the four farms are indicated in Fig. 1. The number and variety of plant and animal species farmed and utilised on smallholder farms is often (surprisingly) high. The integrated, diversified systems typically have some parts that are managed intensively and receive high doses of agrochemicals, whereas other farm components rely on the (re)use and recycling of biomaterials. The conventional labelling of high versus low external input may thus fit some elements within a farm, but not describe adequately the whole system, and the labelling could well be reversed when considering nonmaterial inputs such as labour, information, and skill. For these reasons we did not apply the conventional classification of high versus low external input in the categorisation of farms (Fig. l), as the dichotomy invokes certain farm stereotypes and can be both inaccurate and misleading in the context of smallholder farming.

Integrated and diversified rice fanning

Diversified rice farming

--.

,&m A

\\

c \

L-1’

,---.

c---,

,’ Farm

1 i

(transformations

can occw

B ‘\

‘__/’

/

ffsrma

i I \

C,&,

‘__.’

/

.

in both directions)

Fig. 1. A farming system typology transect indicating the relative positions of the four study farms along a horizontal axis of increasing agroecological complexity.

A qunntitutive

approuch ,fbr a.c.sessing .smallholder

fkms

507

Farm monitoring and data collection

Bioresource flow diagrams (Lightfoot et al., 1994) were used to establish the structure and characteristics of each farm. Figure 2 shows a diagram of one of the four study farms. Such pictorial models can in many instances be drawn by farm household members and serve multiple purposes: they can be used in a participatory mode, where farmers and researchers use the pictures as a focal point for discussing and brainstorming past, present, and future farm management, .’ they quickly provide researchers with a firsthand summary impression of the complexity of a farm system, the various land types that the farm household has access to, and constraints and opportunities for further farm development; and they constitute a basis for structuring information and planning subsequent monitoring and data collection schemes the mode in which the pictorial models were applied within this study. Indigenous natural resource categories (Fig. 3) also played an important role in designing the field monitoring and sampling schemes. Using farmers’ knowledge of natural resources allowed us to identify individual land management units or natural resource types within each farm, and target areas for sampling. This helped ensure that the inherent variability in resource conditions was reflected in the monitoring schemes. Land management units arc typically distinguished by soil type, topography (slope and elevation), and soil water regime, and often have distinct local names. At the outset, individual fields were measured to the nearest metre and mapped. A complete inventory of plants and animals was generated and all stocks were quantified at the beginning and end of the monitoring period. This included the weighing of poultry, girth measurements of ruminants and pigs, and standing biomass assessments of trees. using basic standard mensuration techniques (Philip, 1994; Stewart and Salazar, 1992). Data on inputs and outputs were gathered by the households throughout the monitoring period (12 months). To assist the collection of quantitative information, each household was equipped with a weighing balance and record books. Farm activities and records were discussed and data transferred to researchers’ notebooks during weekly visits. Regular field inspections were made in order to verify and consolidate the information gathered. The growth of field crops, including rice, vegetables, and grasses/weeds, was monitored by the research team through regular field samplings adjusted to farm activity

+RESTORE (Research Tools for Natural Resource Management, Monitoring and Evaluation) a participatory research framework developed at ICLARM uses bioresource flow diagramming as a central tool in generating, monitoring, and evaluating the economic and ecological performance of integrated natural management systems with farmers (Lightfoot et ul., in press; Villaneva et al., in press).

508

J. P. T. Dalsgaard, R. T. Ojicial

A quuntitative

approach ,for assessing smallholder

,furms

509

schedules. Soils were sampled for routine analysis and rice floodwaters for phytoplankton biomass and productivity assessments. The entire monitoring scheme is outlined in detail in Dalsgaard and Oficial (in press). Two large portions of organic biomass were not covered in the data collection scheme, namely roots and soil organisms. Both are substantial and

I

a

‘Alturahin’ 2 (upland 2)

e

-‘white clay’1 I

Fig. 3. An example of a soil sampling layout, covering three different land management and incorporating local knowledge on indigenous soil categories (farm D).

units

510

J. P. T. Dalsgaard,

R. T. Ojicial

both play crucial roles in agroecosystem maintenance and performance. For instance, in the case of leguminous trees, up to 50% or more of tree N may remain below ground after pruning (Sanginga et al., 1995). Most data on crop growth rates and biomass, however, consider only the above ground portion (Mitchell, 1984) as the below ground portion is inherently difficult to monitor and sample. We thus treated the soil largely as a black box and concerned ourselves only with outputs from, inputs into, and the general status of the soil. Other groups of organisms present within the agroecosystems, but not incorporated into the models, included molluscs, reptiles, insects and spiders, rodents, birds, and zooplankton - all usually negligible in terms of their biomass, consumption, and production rates. The (negative) impact of pests and diseases was accounted for in production and harvest figures. Mass-balance modelling From the field data a mass-balance model was generated for each farm. Mass-balance implies that all flows and stock changes are accounted for, for each component, within a given system. In structuring the models we applied ECOPATH,t a software package used extensively for the construction and parameterisation of balanced models of aquatic ecosystems (Christensen and Pauly, 1992a,b, 1993) and now also being applied in the analysis of terrestrial culture systems, with and without aquatic components (Lightfoot et al., 1993; Ruddle and Christensen, 1993; van Dam et al., 1993; Dalsgaard, 1995; Dalsgaard and Oficial, 1995; Dalsgaard et al., 1995; Dalsgaard and Christensen, 1997). Figure 4 shows an ECOPATH flow diagram of one of the four farms. The basis for an ECOPATH farm model is data on the agroecological network of stocks - i.e. the soil, plants, and animals - and the flows - i.e. inputs, outputs, and recycled biomaterials that combine stocks. Conceptually speaking, an ECOPATH model is thus identical to a bioresource flow model. In order to construct an ECOPATH farm model the following basic input parameters must first be calculated for each component identified within the agroecosystem: its size, expressed as the average standing biomass B; growth or production P, expressed as the ratio of P/B; consumption Q, expressed as the ratio of Q/B; harvests and other exports, e.g. losses; and the diet matrix, i.e. the composition of food sources. Plants derive their nutrients from the soil, in the case of legumes partly via fixation, whereas animals typically depend on a range of food sources, including imports into the system. All TECOPATH is available on request from ICLARM, version.

in either a DOS or a Windows based

A quantitative approach for assessing smallholder farms

511

J. P. T. Dalsgaard, R. T. Ojicial

512

these parameters are computed and expressed in a common currency before being entered into the model. The software can to some extent help generate missing parameters. Ecological models are usually either energy or nutrient based (Jorgensen, 1994) and we identified nitrogen (N) as a useful model currency and kg N per ha per year as the basic unit of measurement. Nitrogen contents of farm components, inputs, and outputs were derived either from food and feed composition tables (Giihl, 1981; Food and Nutrition Research Institute (Philippines), 1990) or from analyses of our own field samples. Mass balance also implies that we can derive the overall N balance of a system. To do so, estimates of the magnitudes of fluxes into (dry and wet atmospheric deposition, incoming irrigation water, and nitrogen fixation) and out (volatilisation, denitrification, erosion, run-off, leaching, and straw burning) of the farm systems were derived from secondary data (Roger, 1996). Computing nutrient balances at the farm level is, however, problematic and data from on-farm investigations are scarce. Accurate nutrient balance studies are perhaps more feasibly pursued at higher spatial scales within the landscape, e.g. at watershed or regional levels - see, for instance, Smaling and Fresco (1993).

PERFORMANCE

INDICATORS

The ECOPATH software integrates ecosystems modelling with mainstream quantitative systems ecology. Built-in analytical routines compute aggregate system properties, or attributes, which can be used to assess the state of an ecosystem. Many of these are derived from Odum’s (1969, 1971) seminal analysis of development and maturity in natural ecosystems. Others stem from more recent explorations into ecosystem growth and development (Ulanowicz, 1986). Several are applicable within an agroecological context and can help provide quantitative insights into the agroecosystem’s performance. Before introducing these different attributes we need, however, to make one important qualification. As evident from the ECOPATH diagram in Fig. 4, plant and animal components are split into different categories. In some cases a component constitutes an individual species (e.g. rice); in other cases it is made up of two or more functionally similar species, such as ‘poultry’. For the smallholder farm scenarios investigated here, we derived the following list of model components, which we refer to as ‘agricultural guilds’:+ rice, vegetables, grasses/weeds, azolla, phytoplankton, fruit trees, multipurpose trees, bamboo, large ruminants, pigs, poultry, and tin ecology,

the guild concept

refers to functionally

similar species (Begon et al., 1990)

A quantitative approach for assessing smallholder farms

513

fish. By agricultural guilds we refer to groups of organisms distinguished by their ecological attributes and economic functions within the agroecosystem. The guilds reflect the structure and function of the agroecosystem as designed by the household. They thus mirror distinct units of farm management while at the same time providing convenient units of data collection, modelling and analysis for the researcher. ‘Detritus’ in Fig. 4, refers to the farm’s soil resource base, whereas ‘BNF’ signifies biological N fixation by the agroecosystem. We were concerned with two types of investigations, namely the agroeconomic and the agroecological performance of the four farms. For a comparative analysis of the systems the following lists of attributes was derived. Agroecological indicators+ Species richness

A count of the number of species is the most basic diversity measure and provides a first rough indicator of the biological complexity of an agroecological system. There are several possible benefits to managing many species and taxa, such as complementary and fuller use of resources, pest protection, and compensatory growth (Ewel, 1986). The extent to which potential benefits are realised depends on species choice, system design, and management. A high number of species does not in itself guarantee efficient resource use. Functional agricultural

diversity

Biological diversity measures consider not only the number but also the relative abundance of each species. We did not, however, collect data on a species basis but on the basis of agricultural guilds. These were subsequently used as the basis for computing the functional agricultural diversity for each farm, using Shannon’s index (Magurran, 1988). The abundance of each unit (guild) was quantified on the basis of its average standing biomass B, measured in kg N per ha. This way of computing biological diversity, across taxonomic groups, is unconventional. Diversity is generally seen as an indicator of system well-being: the higher the diversity the better the state of the system. In a natural ecosystem diversity is expected to increase as the system matures (Odum, 1969, 1971) and a high biodiversity is sometimes seen as an indispensable feature of sustainable agroecosystems.

‘Those attributes

marked

with an asterisk were computed

by the ECOPATH

software.

514

J. P. T. Dalsgaard, R. T. Ojicial

Eficiency

Efficiency is defined as the output/input ways:

ratio and was here computed in two

(a) ‘apparent efficiency’, calculated as the ratio of outputs, including only harvests removed from the system, over inputs, including only feeds and fertilisers imported into the system; (b) ‘actual efficiency’, computed as the ratio of all harvested outputs removed for consumption, sale, and storage from the farm system over all imports (including feed and fertiliser imports, BNF, wet and dry deposition, and run-on (sedimentation) with incoming irrigation water) into the system; Nutrient use efficiency is expected to increase in natural ecosystems with maturity and the closing of nutrient cycles. In agroecological systems, efficiency frequently decreases over time as the systems lose their ability to capture and store nutrients - an indication of resource depletion and soil degradation. From a sustainability point of view, the output/input ratio should remain constant or increase over time (Munasinghe and Shearer, 1995). System harvest index

The system harvest index, computed as the ratio of ‘net system yield’ over ‘net system production’ (see below), is another expression of the efficiency of a system and its ability to convert available resources into a desired output. Productivity*

Productivity is probably the most widely applied agricultural performance indicator. We identified four productivity measures: (a) ‘net rice grain yield’, measured in kg per ha rice land; (b) ‘net rice grain N yield’, measured in kg N per ha rice land; (c) ‘net system N yield’, computed as net N harvests extracted from the whole farm; (d) ‘sum of all production’, expressing the sum of all organic material produced by the system, whether harvested, retained as stock, or returned to the soil for decomposition, and measured in kg N per ha of farmland. Net productivity and thus yield decreases in natural ecosystems as these mature (Odum 1969), and a lowered productivity is sometimes held to be an inevitable trade-off of ecologically sound agriculture, which tries to mimic natural ecosystems more closely.

A quantitative approach for assessing smallholder farms

515

Total system biomass* The average standing biomass of plants and animals is expected to increase with ecological succession in natural ecosystems (Odum, 1969, 1971). Biomass maintenance or increase may be part of a long-term strategy by farmers investing in natural resource capital in order to reduce risk, buffer the system against the adverse effects of environmental stress and perturbations, and secure future productivity. Stock build-up may, however, also be a shortterm goal of farmers hoping to make a quick return on investments under uncertain environmental and socioeconomic conditions. Agroecosystems dominated by large and longer-lived organisms such as trees and livestock, i.e. farms that emulate some of the characteristics of more developed ecosystems, are expected to have a comparatively high biomass. System biomass, however, is also a result of the quality and availability of resources, particularly that of water. Good water supply will, other things being equal, yield higher primary and secondary production, and thus a higher standing biomass, than will a poor water supply. Nutrient cycling’ Nutrients are recycled within (agro)ecosystems. Component integration and reuse of bioresources, such as manures and crop by-products, help close nutrient cycles and reduce losses. This, potentially, makes for more efficient resource use. Finn’s cycling index (Finn, 1980) expresses the recycled fraction of total throughput. It takes account of all cycles, including nutrients reused through farmer-managed bioresource flows, e.g. the feeding of crop by-products to animals and the application of manures on crops, as well as through naturally occurring cycles as in nutrient uptake during plant growth and subsequent release through decomposition. It can be seen as an expression of system integration and was originally intended to quantify one of Odum’s (1969) properties of ecosystem maturity. Its interpretation, however, is not straightforward. For instance, the index varies with currency, nutrients being recycled more than energy (Christensen and Pauly, 19926). Nutrient throughput* A system’s throughput of nutrients is computed by ECOPATH as the sum of consumption (including imports), exports, and flows to detritus (Christensen and Pauly, 19923), and is seen to represent the size of the system in terms of flow (Ulanowicz, 1986). The measure is further used in the computation of the biomass/throughput ratio (see below). Primary production/biomass (P/B) * Primary production over biomass, the P/B ratio, decreases in natural ecosystems as these evolve and accumulate biomass (Odum, 1969, 1971). Farms

516

J. P. T. Dalsgaard, R. T. Ojicial

are often likened to immature ecosystems and we would therefore expect high P/B ratios for such systems. If, on the other hand, an avenue towards a sustainable agriculture is through mimicking the characteristics of maturing natural ecosystems, then we should strive to design and develop agroecosysterns with comparatively low P/B ratios. Biomass/throughput (B/E)*

The total ecosystem biomass B supported by the available energy flow E is expected to increase as an ecosystem approaches maturity (Odum, 1969, 1971). We assumed here that biomass supported by available nutrient flow N follows a similar pattern of behaviour. If agroecological sustainability is related to ecosystem maturity, we should look for farms with comparatively high B/E ratios. Agroecosystem N balance

The N balance of each agroecosystem was computed from the following equation (Dalsgaard, in press): (feed and fertiliser inputs) + (BNF) + (run-on with incoming irrigation water) + (dry and wet atmospheric deposition)-(net harvest)-(erosion + run-off)-(leaching)-(volatilisation + denitrification). The nutrient balance is an important indicator of the sustainability of a system. Agroeconomic indicators Gross margin

The gross margin is defined as the gross income (cash and noncash) minus variable costs (cash and noncash). We computed the gross margin of each farm by ascribing monetary values to all flows, including both material and labour (family and hired labour) flows, using local prevailing market prices. Returns to labour

It is regularly assumed that diversification and integration requires additional labour inputs, resulting in smaller returns to labour and posing constraints to the wider adoption of integrated farming strategies.

RESULTS

AND DISCUSSION

Summary field data for each of the four farms format in Figs 5-8. The quantified ecological are shown in Tables 1 and 2. For comparative Table 1 the results of an earlier ECOPATH study ecosystems (Lightfoot et al., 1993). This particular

are presented in transect and economic attributes purposes we included in of N flows in wetland rice model was based on data

Fig. 5. A transect

overview

Net yield

Nutrient imports (feeds and fertilizers)

-=mponenb

!

Plant end animal

rice3fs crops)

- phyloplankton

- r,ce(2 crops) weeds - Dhytoolankton

of the characteristics

and performance

of farm A, a monoculture

135 kg urea, 275 kg complete fertlkzer (14-14-14) 35 kg ammonium phosphate ~___ ___ 7,450 kg nce - 600 ka rice r ~~~ ~

~___

- rainfediirrigated - intermittent irrigation

Lowland (0.10 ha)

I

(total area I .36 ha)

dry season crop)

rice agroecosystem

: ;~r.;s(l

Sloping land’river bank (0.02 hal

Fig. 6. A transect

management

(Primary national irrigation canal)

Irrigated

- ra~nfecU~r”galed

- ‘black clay’ -clay

Upland (1.18 ha)

I ,I

1

I 1

-

crops)

phyioplaokton azolla

nce(2.33 - weeds

. ramfe&rigated - irrigated

‘black clay’ - clay

L&and (0.21 ha)

--

rice. watermelon, bitter gourd, tomato, bottle gourd, long beans, maize, chili, banana

and performance

50

50

kg kg kg kg kg kg kg 20 kg kg

6,000 4,ooO 1,600 1.100 75 : 50

of farm B, a diversified

1,750 kg we

-rice: 150 kg urea, 100 kg complete fertilizer (14-14-14) -vegetables: 200 kg urea, 100 kg 143 urea, 225 kg complete fertilwer (14-14-14)

1: ffieys 1- phytoplankton

\ -rice-fish 1-vegetables , bottle

( - rice (2.33 crops) (0.12 ha, wet ( seeson only) (bitter gourd, gourd, chili pepper, long bean, maize, tomato, I watermelon) (banana, coconut)

! ’ ’

I

I

1

1 I

overview of the characteristics

Net yield

Nutrient imports (feeds and fertilizers)

Plant and animal components

dry seaeo”

water supply wetseason

soiltype farmer researcher

.and mit

-

-

clay’

-

-

kg rice bran

smallholder

clay’

315 kg mango

: ~eitte5(mango)

rainfed

- rainfed

- ‘black

sloping landhwer bank (0.02 ha)

rice agroecosystem

150 kg tilapia 315 kg water spinach 5kg eggplant 50 kg banana 15 kg papaya

- 120

PaPaYe) weeds phytoplankto”

- vegetables (eggplant, water spinach) - fruit trees (banana,

(Me tilapia, wet season oniy)

- fish

irrigated

- rainfed/irrigated

- ‘black

Fan” ponds (0.10 ha incl. banks)

(total area 1.51 ha).

A quantitative approach for assessing smallholder farms

519

Fig.

8.

A transect

overview

Net yield

Nutrient imports (leedsand feriflizers)

and

20 kg commercialpoultryleed 800 kg wmmercisl pig leed 6,ooOkg grassfor ruminanta

mango,papaya,tamannd) multipurposetrees (Acada, bgi Leucssna)

-poullry(ti&en ducks geese,turkeyaj

(rainled)

4

vegetables(common bean, eggplant,Ion bean,sweet potatoP

I

Lvhiteclay

U land2 $32 ha,

I

I

performance

of farm D, a (total area 2.75 ha).

~lt?OO kg nce

-115kgurea

“CC

-phyioplankton

:;~~scmP)

I

I

I

and

-600 kg ace

‘blackclay -w

diversified

W

- rainfediintermitientirngation ramledfallow

3,750 kg rice 50 kg sweet polslo tops 6 kg sweet potato tubers 30 kg eggplant 12 kg long beans 11kgcommon beans

-

L,

e-----_-

‘red clay’ -clay

-‘loam’ -rainled

U landi $39 ha)

Homestead 10.26ha1

of the characteristics

Plantandanimal components

wetseascdl - dry season

water supply

“:$a - researcher

Lamidmanagemenl

integrated

1

bank

jaw plum: manso;

smallholder

psrys) - mu bpurpoaetrees gridkidleucaena) weeds

- fruil trees fbanans.

ralnfed

-rsinlsd

‘white clay’ -clay loam

Sloping $I/;,“’

rice agroecosystem

“Figures ‘Cycling

843 2.4 0.16 +46

716 2.8 0.12 -2

785 1.5 0.21 +72

339 164 0.38

6.7 (3.3) 52 (26) 45 (26)

0.17 0.13

0.27

I .06

Itl

385 1.3 0.26 +1

195 99 0.43

3.6 32 39

0.40 0.20

1.30

28 1.62

Farm C diversljied and integrated rice system (2.76 ha)

Rice Farm Scenarios

Farm B diversified rice system (1.51 ha)

Smallholder

323 88 0.25 (0.44)h

6.0 (3.2) 44 (23) 43 (22)

in parentheses indicate productivities after subtraction of dry season rice crops. index value if rice straw were reincorporated instead of burnt.

6.5 (3.25) 65 (32.5) 65 (32.5)

1.19 0.14

0.33

0.44 0.26 0.16

4 0.70

Farm A monoculture rice system (1.36 ha)

3 0.51

Philippine monoculture rice system (I.00 ha)

TABLE 1 Indicators across Different

Nutrient throughput (kg N per ha per year) P/B ratio (/year) B/E ratio (/year) Agroecosystem N balance (kg N per ha per year)

Performance

395 131 0.45

diversity

~~. __~..~

Agroecological

Actual efficiency System harvest index Productivity” Net rice grain yield (tonnes per ha rice area per year) Net rice grain N yield (kg N per ha rice area per year) Net system N yield (kg N per ha farm area per year) Sum of all production (kg N per ha per year) Total system biomass (kg N per ha per year) Nutrient cycling (Finn’s index)

Species richness Functional agricultural Efficiency (output/input) Apparent efficiency

~~

Quantified

351 0.7 0.46 -9

149 161 0.39

2.9 30 33

0.38 0.22

0.77

32 1.56

Farm D diversified and integrated rice system (2.75 ha)

s

8 2

b $ % z. S 2. R a % 2 a 0 % Y a E 2 2. 3

Across

141 (72) 94 (52)

47

Person days per ha Total labour input Family labour input

Returns to labour Returns to total labour (PP/day)

TABLE 2 Different Smallholder

145

110 100

304 276

8500 16000

Farm C diversljied and integrated rice system (2.76 ha)

Rice Farm Scenarios

of dry season rice crops.

118

165 (121) 115 (101)

249 (182) 174 (152)

1500 19500

Farm B divers$ed rice system (1.51 ha)

“US$ 1 = - 26PP (Philippine peso). bFigures in parentheses show labour requirements after subtraction Total labour input includes hired, exchange, and family labour.

192 (98) 128 (71)

6500 6500

Total labour inputc Family labour input

Labour input@ Person days per farm

Gross margin (PP/ha per year) Rice component Whole farm system

Indicators

Farm A monoculture rice system (1.36 ha)

Agroeconomic

155

100 82

215 226

1000 15500

Farm D diversljied and integrated rice system (2.75 ha)

z? g.

Ir ‘a .Y b I% ds” :: _L 3 .Y

A quantitative approach for assessing smallholder farms

523

from on-station research on the trophic interactions between organisms (rice, weeds, phytoplankton, zooplankton, insects, benthic and soil organisms) within the rice floodwater ecosystem and was adapted to fit the level of whole farm analysis proposed here. + It represents a generic model of Philippine monoculture rice farming. In an attempt to adjust, in the analysis, for the unequal access to water across the four study farms (farms A and B received ample water supply during the wet season and almost regular supply during the dry season, whereas farms C and D received intermittent irrigation water during the wet season only and no irrigation water at all in the dry season) and provide the basis for a fairer comparison of the productive capacity of the four systems, we subtracted the dry season rice crops from farms A and B - the results shown in parentheses in Tables 1 and 2. Agroecological

indicators (Table 1)

Species richness

We counted weeds and grasses as one ‘species’ and all phytoplankton as one ‘species’ only. Field inspections did show a high number of weed species present, with farmers capable of identifying and naming up to 15-l 6 different kinds of weeds within and around rice fields. The rice floodwater analyses showed up to 19 genera of phytoplankton present at any one time, including diatoms and blue-green algae. Discounting these observations, we observed large differences in agricultural species richness across the four study farms and an eightfold increase from farm A to farm D. The number of 32 species on farm D, however, is still low compared, for instance, with traditional homegarden agroecosystems, where the number of plant species per ha regularly exceeds 100 (Hoogerbrugge and Fresco, 1993). Functional agricultural

diversity

The observed pattern conforms to expectations, with agricultural diversity increasing as farmers add and integrate new farm components. We found no comparable data in the literature. +The following adjustments were made to the original dataset: insects, snails, zooplankton, benthic organisms and soil microbes were removed from the model; a second rice crop identical to the first crop was added, assuming a system with two crops per year; fertiliser rates were doubled accordingly; yields were adjusted down from 4.0 tonnes of grain per crop per ha to 3.25 tonnes per crop per ha - equivalent to the average wet season yield on the four study farms; a value of 1500 kg N per ha detritus biomass, as found on the four farms, was assumed; an N content of 1% in rice grain was assumed - equivalent to the average grain N content on the four farms; and phytoplankton was given a P/B ratio of 100, assuming a near daily turnover of biomass.

524

J. P. T. Dalsgaard, R. T. Oficial

EfJiciency

(a) Farms A and B imported 132 kg N per ha and 167 kg N per ha, respectively, in inorganic fertilisers, whereas farms C and D imported only 31 kg N per ha and 43 kg N per ha, respectively, via inorganic fertilisers and feeds. The Philippine monoculture rice system applied 148 kg inorganic N per ha per year. The low application rates on farms C and D explain their higher apparent efficiency. The high value on farm C - an apparent efficiency above 1.OO- stems primarily from a very good rice harvest. (b) Taking into account all possible fluxes of N into and out of the systems we find actual efficiencies around 1.0, ranging from 0.19 to 0.39. The data show a marked increase in resource (nutrient) use efficiency with diversification, recycling, and integrated nutrient management. System harvest index

The index increases with diversification and integration. This is surprising in as much as we usually expect a high harvest index to be a feature, or at least a prime objective, of modern, chemical-intensive monoculture farming. The results are encouraging in that they support the notion of a more efficient use of resources (nutrients) within an integrated agriculture. Productivity

(a) ‘Net rice grain yield’ was highest, where continuous cultivation was possible. In the case of farm B, the abundance of water permitted almost five rice crops in two years, or just under 2.5 crops per year. When adjusting for the unequal access to water across the four farms by subtracting the dry season rice crop production on farms A, B, and the Philippine rice system, these systems are matched in performance by the integrated farms (adjusted figures are shown in parentheses in Table 1). (b) ‘Net rice grain N yield’ follows a similar pattern to that of net grain yield, albeit the relative performance of farms C and D has improved, due to a superior grain quality. We found grain N contents to vary from 0.8 to 1.1% depending on cultivar, with the highest N contents on farms C and D. (c) ‘Net system N yield’ was highest from the monoculture rice systems and farm B, i.e. the systems applying large amounts of chemical fertilisers and capable of year-round rice cultivation. The differences across the five systems were clearly reduced, however, upon inclusion of harvested outputs from the nonrice components. When adjusting for unequal water access, the integrated farms actually surpass the other systems.

A quantitative approach for assessing smallholder~farms

525

(d) The ‘sum of all production’ values underline the importance of adequate water access: the better the water supply the higher the total harvested and nonharvested biomass production. Total system biomass The comparative analysis illustrates the potential role of perennials and livestock, i.e. of larger, longer-lived organisms, in maintaining biomass stock. Such plant and animal groups were present on farms B. C and in particular on farm D, which, despite the most erratic irrigation water supply of all four farms, still managed to maintain a comparatively high average standing biomass. Nutrient cycling These results highlight the importance of the naturally occurring nutrient flows within farms, such as N uptake in and release from field crops, relative to the so-called bioresource flows (crop residues fed to animal, manures applied to field crops, etc.) depicted as arrows in Fig. 2. Quantitatively speaking, the latter are of minor importance only in the cases presented here. Although the overall trend confirms our intuitive expectation, namely an increase in N cycling with integration, the relative differences across the farms are less pronounced than anticipated. The explanation for this we find in the circumstances surrounding these farm systems: in rice-based agroecosystems very large amounts of biomass are produced within the rice flood water in the form of rice plants, weeds (on farms A and B, weeds accounted for 38-39% of total system biomass production), and phytoplankton. Where crop residues and weeds are reincorporated into the soil these flows easily dwarf other, farmer-managed, bioresource flows. Although animals constituted key elements in the two integrated systems, they were not managed for maximum biomass production or kept in numbers sufficiently large to warrant intensive cycling of crop residues and other plant by-products. Manures were not systematically returned to fields, but left in the homestead areas and thereby essentially lost from the production system. The burning of rice straw on farm A contributed substantially to the loss of N. Reincorporating, instead of burning, straw would result in an increase in farm A’s N cycling index from 25 to 44%, comparable to that of the diverse and integrated farms. This suggests that careful handling of weeds and residues can help ensure sound nutrient management particularly in monoculture rice. Nutrient throughput The large throughputs in the two monoculture systems and farm B were primarily due to their sizeable inorganic fertiliser imports and the ability to

526

J. P. T. Dalsgaard, R. T. Ojcial

produce rice continuously. In terms of system throughflow, the diverse and integrated farms were thus comparatively small, as one would expect where agroecosystems are less open and nutrient cycles more closed. P/B ratio This attribute behaved very much as anticipated: the more the farm resembles a maturing ecosystem, i.e. as tree and animal components are integrated into the agroecosystem, the lower the P/B ratio. The monoculture rice farms can be likened to a natural ecosystem in an early successional phase, when it is dominated by pioneer (grass) species, whereas the diversified and integrated farms can be compared to an ecosystem in a more developed evolutionary stage. B/E ratio This measure reaffirms our intuitive perception: the more the agroecosystem resembles a developed, maturing ecosystem the higher the B/E ratio. Agroecosystem N balance

These results suggest that (near) positive N balances can be maintained without resorting to high fertiliser application rates. What they also indicate, however, is that high application rates may result in high losses to, and thus pollution of, the surrounding environment. The application rates for the monoculture rice systems and farm B were more than 100 kg higher than for the integrated systems, yet their calculated final balances were at best around 80 kg higher and in the worst case similar to those of the integrated farms. Despite the low fertiliser imports into farms C and D, they still managed to maintain balances close of zero due to only minor losses. These balance computations support the previous results on efficiency, productivity, and nutrient cycling. Seen together the data suggest that integrated natural resources management can be efficient, productive, and environmentally sound (better recycling and fewer losses of nutrients). The quantitative findings thus support our qualitative perceptions about the potential advantages of integrated farming. Agroeconomic indicators (Table 2) Gross margin

The economic analysis supports the notion that diversification and integration may benefit farm performance. Least income was generated by the monoculture rice system (farm A), whereas farm B derived most of its income from crops other than rice, namely vegetable cash crops. Its rice crop was adversely affected by snail damage and poor water management despite

A quantitative approach for assessing smallholder,farms

527

ample supply. The one rice crop on farm C produced a very good yield generating more than half of farm income. Despite the poor performance of the rice crop on farm D, due to insufficient water supply and a late pest attack, the system still performed well from the sale of livestock, wood, and bamboo products, and the value of poultry and various other outputs consumed onfarm. The economic contributions from fish, vegetables, fruit trees, and pigs were generally small to negligible. The role of these components may nevertheless still be important when considering other performance aspects such as household nutrition (Ruddle, 1996; Ruddle and Prein, 1997). Labour inputs In terms of farm labour? requirements the results show an increase with increasing farm size as expected. Total labour inputs per ha show a decrease with increasing farm size, whereas family labour inputs per ha show no changes with farm size, diversification, or integration. Labour is primarily hired in for rice transplanting and harvest. The comparison of labour requirements across the different farm types was somehow complicated by the fact that the need for labour on farms C and D was comparatively low during the 6 months dry season from December to May. Due to the absence of irrigation water, 83 and 65%, respectively, of their farmland was left fallow. Compensating for this imbalance by subtracting the dry season rice crops on farms A and B, we derived the figures shown in brackets in Table 2. The adjusted data suggest an increase in the demand for total labour, as well as for family labour, as the agroecosystem becomes increasingly complex. This conforms to expectations in that animal, vegetable, tree and other minor (new) enterprises are usually managed exclusively by members of the farm household. Returns to labour The payback to labour was clearly highest on the diverse and integrated farms. Farms C and D were less intensively managed than farms A and B for a number of reasons, including water shortage, the farmers’ age, and insecure tenure/future. The data nevertheless strongly suggest that it pays to invest time, effort, and skill in experimentation and intensification through diverse and integrated farming, where labour is available and affordable.

‘Labour is here defined as work undertaken on-farm in connection with crop and animal production. Examples of activities include land preparation, planting, fertilising, weeding, and harvesting. Post-harvest activities (e.g. the milling of rice or the slaughter of animals) were excluded, as were off-farm and nonfarm activities except for the herding of livestock and the collection of livestock fodder. Labour inputs were based on the households’ own estimates and recordings.

528

J. P. T. Dalsgaard, R. T. Oficial

CONCLUSIONS The framework presented shows some of the potential dangers and shortcomings inherent within a narrow enterprise focus when assessing the performance of smallholder agroecological systems. It demonstrates the merits of a systems-oriented approach and the need not only to investigate the individual farm components but equally importantly their interactions (Rykiel, 1984). Taking an agroecosystems approach compels the researcher to acknowledge and appreciate the ecological diversity and complexity of smallholder farms. Incorporating farmer knowledge on natural resource conditions and management supports this effort. Our findings support the notion that diversification and integration - i.e. that which is often perceived as ecologically sound farming - can foster resource efficient, manageable and productive systems. Quantitative indicators for assessing the ecological performance of farms are emerging (Dalsgaard et al., 1995). Applying such indicators within a framework as presented here can help us address the issue of ecological soundness in agriculture in a rigorous, systematic, and quantitative manner. Passing judgement on the nature and performance of agroecological systems on the basis of qualitative assessments only is fallible. This is illustrated in the case of nutrient cycling: what appears to be, and what we intuitively would classify as, the more integrated agroecosystem may not necessarily be so in quantitative terms. The amount of nutrients cycled via bioresource flows as depicted in a bioresource flow diagram is often small compared to the material cycling that otherwise occurs within an agroecosystem. In this respect the bioresource flow diagrams can be deceptive. This, however, should not lead us to underestimate the importance of active recycling and efficient use of wastes and by-products. Even when bioresource flows make up only a small fraction of the nutrients cycled they may still play an important catalytic or supportive role in the same manner that (small) external nutrient injections can enhance the performance of and help regenerate degraded agroecosystems (Kessler and Moolhuijzen, 1994). On the other hand, where bioresource flows carry large quantities of organic biomass and nutrients their impact on system performance is likely to be substantial. The advantages of viewing farms as agroecosystems when assessing their performance are also evident in the investigation and comparison of their productive capacity. The main component (rice) may not play as visible a role in integrated and diversified systems as in monoculture systems; yet, it often supports other components within the farm, from which substantial additional outputs are generated, thereby playing an important indirect role in overall system performance. Focusing exclusively on a system’s main component may lead us to overlook or underestimate the potential role and

A quantitative approach for assessing smallholder farms

529

contribution of other components; by adopting a systems perspective we are more likely to avoid such ‘blind spots’. The comparative analysis also supports the notion that mimicking the structure and function of ecosystems when designing agroecological systems can benefit both the ecological and agronomic performance of farms. Others have reached similar conclusions. In their study of integrated agricultureaquaculture farms, shrimp farming, and coastal culture of seaweeds, Folke and Kautsky (1992) state that ‘the more a cultivation system recognises and mimics natural ecosystem functions the less resource inputs are required and the less environmental effects can be expected’. This general guideline, or management principle, may thus have applicability across a broad range of natural resource scenarios. It does not, however, support the notion of eliminating external inputs - as mentioned above these may be a necessary ingredient in a regenerative agriculture - and it does not offer cookbook recipes for agroecosystem design and development. Selection and combination of plant and animal components will always be site and resource specific. Farmers often have ideas on how best to go about this. The outlined approach and analysis, while first and foremost intended as a research tool as opposed to a farm management tool, also helps pinpoint scope and areas for improvement in management: Farm A had a large untapped potential for diversification and integration. The farmer recognised this and implemented, on his own initiative, various changes towards the end of the monitoring period: a piece of fallow land was leased for vegetable cultivation using rice straw as a mulch; a small fishpond was constructed near the stream for future stocking; and a pigsty was built. The farmer also considered composting rather than burning straw, but identified labour as a major constraint. ?? On farm B, a process of transformation had been initiated a few months before we established contact with the household. This had resulted in the recent construction of fishponds and the planting of fruit trees and vegetables on rice bunds and pond embankments. Towards the end of the dry season a large rice-straw based compost heap was erected for use on the next wet season rice crop. The pond enterprise, however, finally had to be abandoned after a controversy over land use with the landowner. ?? The concepts of diversification and integration were far from novel on farms C and D. They had been exposed to such ideas through the field activities of an NGO engaged in the development of low external input rice production systems. Still, opportunities existed for further integration and intensified resource use on both farms. Most apparent was the underutilisation of animal manures and multipurpose tree prunings for

??

530

J. P. T. Dalsgaard, R. T. Ojcial

cornposting, green manuring, and livestock feeding. Both households, headed by ageing farmers, listed shortage of labour and insecure tenure as constraints to further experimentation and management changes. Within the range of farm sizes covered in this study, from just over 1 ha to less than 3 ha, increased complexity through diversification and integration appears to be a feasible strategy, given readily available labour and secure tenure. These two factors, labour shortage and insecure tenure, were identified by the farmers as the major obstacles to changes in farm management and planning. Constraints and opportunities, as well as ideas for improved systems design can be discussed with households in bio-resources flow diagramming sessions, where the results of the previous production and monitoring period are fed back to (groups of) farmers, for their comments and consideration, when planning the coming season or year. This interaction may also assist researchers in eliciting a wider range of performance (sustainability) indicators of relevance from the farmers’ point of view. Ideally, indicators should be appreciated by all participants. In our experience, measures such as species richness (complexity), productivity, gross margin, and cash income provide a good point of departure for discussing farm performance and indicators with households.

ACKNOWLEDGEMENTS We wish to thank the four Philippine farm households, the International Institute of Rural Reconstruction (IIRR) for field collaboration, and Villy Christensen and Clive Lightfoot for inspiration, ideas, and inputs into developing the monitoring and modelling framework. The project was funded by Danida (Danish International Development Assistance). ICLARM Contribution No. 1264.

REFERENCES Altieri, M. A. (1995) Agroecology: The ScientiJic Basis of Alternative Agriculture, 2nd edn. Westview Press, Boulder. Beets, W. C. (1990) Raising and Sustaining Productivity of Smallholder Farming Systems in the Tropics: A Handbook of Sustainable Agricultural Development. AgBC Publishing, Alkmaar, The Netherlands. Begon, M., Harper, J. L. and Townsend, C. R. (1990) Ecology. Individuals, Populations and Communities, 2nd edn. Blackwell Scientific Publications, London. Christensen, V. and Pauly, D. (1992a) ECOPATH II - a software for balancing steady-state ecosystem models and calculating network characteristics. Ecological Modelling 61, 169-185.

A quantitative approach,for assessing smallholder,farms

531

Christensen V. and Pauly, D. (1992b) A guide to the ECOPATH II program (version 2.1). ICLARM Software 6. International Center for Living Aquatic Resources Management, Manila. Christensen, V. and Pauly, D. (eds) (lP93) Trophic models of aquatic ecosystems. ICLARM Conference Proceedings 26. International Center for Living Aquatic Resources Management, Manila. Conway, G. R. (1985) Agroecosystem analysis. Agricultural Administration 20, 31 55. Cox, G. W. (1984) The linkage of inputs to outputs in agroecosystems. In Agricultural Ecosystems: Unifring Concepts, ed. R. Lowrance, B. R. Stinnner and G. J. House, pp. 187-208. John Wiley & Sons, New York. Dalsgaard, J. P. T. (1995) Applying systems ecology to the analysis of integrated agriculture-aquaculture farms. NAGA, The ZCLARM Quarterly 18(2), 15-19. Dalsgaard, J. P. T. (in press). Tracking nutrient flows in a multi-enterprise farming system with a mass-balance model (ECOPATH). In Managing Soil Fertility for Intensive Vegetable Production Systems in Asia. Asian Vegetable Research and Development Centre, Tainan, Taiwan. Dalsgaard, J. P. T. and Christensen, V. (1997) Flow modeling with ECOPATH: providing insights on the ecological state of agroecosystems. In Applications qf Systems Approaches at the Regional and Farm Levels, ed. P. S. Teng, M. J. Kropff, H. F. M. ten Berge, J. B. Dent, F. B. Lansigan and H. H. van Laar, pp. 203-212. Kluwer Academic Press. Dalsgaard, J. P. T. and Oficial, R. T. (1995) Insights into the ecological performance of agroecosystems with ECOPATH II. NAGA, The ZCLARM Quarterly 18(3). 26-27.

Dalsgaard, J. P. T. and Oficial, R. T. (in press). Quantitative modeling and performance analysis of agroecological systems with ECOPATH. ICLARM Technical Report 53. Dalsgaard, J. P. T., Lightfoot, C. and Christensen, V. (1995) Towards quantification of ecological sustainability in farming systems analysis. Ecological Engineering 4, 181-189.

dela Cruz, C. R., Lightfoot, C., Costa-Pierce, B. A., Carangal, V. R. and Bimbao. M. P. (1992) Rice-fish research and development in Asia. ICLARM Conference Proceedings 24. Douglass, G. K. (ed). (1984) Agricultural Sustainahlity in a Changing World Order. Westview Press, Boulder, CO. Ewel, J. J. (1986) Designing agricultural ecosystems for the humid tropics. Ann. Rev. Ecol. Syst. 17, 245-27 1. Finn. J. T. (1980) Flow analysis of the models of the Hubbard Brook ecosystem. Ecology 6, 562-57 1. Francis, C. A. (1986) Biological efficiencies in multiple-cropping systems. Advances in Agronomy

42, l-42.

Food and Nutrition Research Institute (Philippines) (1990) Food composition tables: recommended for use in the Philippines. Food and Nutrition Research Institute, Department of Science and Technology, Manila. Folke, C. and Kautsky, N. (1992) Aquaculture with its environment: prospects for sustainability. Ocean and Coastal Management 17, 5-24. Gliessman, S. R. (ed.) (1990) Agroecology: Researching the Ecological Basis ,for Sustainable Agriculture. Springer-Verlag, New York.

J. P. T. Dalsgaard, R. T. Ojicial

532

Gliessman, S. R., Garcia, R. and Amador, A. M. (1981) The ecological basis for the application of traditional agricultural technology in the management of tropical agro-ecosystems. Agro-Ecosystems 7(3), 173-185. Gohl, B. (1981) Tropical Feeds: Feed Information Summaries and Nutritive Values. FAO, Rome. Guo, J. Y. and Bradshaw, A. D. (1993) The flow of nutrients and energy through a Chinese farming system. Applied Ecology. 30, 86-94. Harris, F. (1996) Intensification of agriculture in semi-arid areas: lessons from the Kano close-settled zone, Nigeria. Gatekeeper Series No. 59, International Institute for Environment and Development, London. Hoogerbrugge, I. D. and Fresco, L. 0. (1993) Homegarden systems: agricultural characteristics and challenges. Gatekeeper Series No. 39, Internaional Institute for Environment and Development, London. Jorgensen, S. E. (1994) Fundamentals of Ecological Modelling, 2nd edn. Elsevier, Amsterdam. Kessler, J. J. and Moolhuijzen, M. (1994) Low external input sustainable agriculture: expectations and realities. Netherlands Journal of Agricultural Science 4243,81-194.

Lightfoot, C., Roger, P. A. and Cagauan, A. G. (1993) Preliminary steady-state nitrogen models of a wetland rice-field ecosystem with and without fish. In Trophic Models of Aquatic Ecosystems, ed. V. Christensen and D. Pauly, pp. 56 64. ICLARM, Manila. Lightfoot, C., Prein, M. and Lopez, T. (1994) Bioresource flow modeling with farmers. ZLEZA Newsletter 10(3), 22-23. Lightfoot, C., Bimbao, M. P., Lopez, T. S., Villanueva, F. D., Orencia, E. A., Dalsgaard, J. P. T., Gayanilo, Jr, F. C., Prein, M. and McArthur, Jr, H. J. (in press). Research tools for natural resource management, monitoring and evaluation (RESTORE): field guide. ICLARM Software 9, Vol. 1. International Center for Living Aquatic Resources Management, Manila. Little, D. and Muir, J. (1987) A Guide to Integrated Warm Water Aquaculture. Institute of Aquaculture Publications, University of Stirling, Stirling, UK. Loucks, 0. L. (1977) Emergence of research on agro-ecosystems. Ann. Rev. Ecol. Syst. 8, 173-192.

Lowrance, R., Stinner, B. R. and House, G. J. (eds) (1984) Agricultural Ecosystems: Unifying Concepts. John Wiley & Sons, New York. Magurran, A. E. (1988) Ecological Diversity and Its Measurement. Croom Helm, London. Mitchell, R. (1984) The ecological basis for comparative primary production. In Agricultural Ecosystems: Unifving Concepts, ed. R. Lowrance, B. R. Stinner and G. J. House, pp. 13-52. John Wiley & Sons, New York. Munasinghe, M. and Shearer, W. (eds) (1995) Defining and Measuring Sustainability. The Biophysical Foundations. The World Bank, Washington. Odum, E. P. (1969) The strategy of ecosystem development. Science 164, 262270. Odum, E. P. (1971) Fundamentals of Ecology. W. B. Saunders, Philadelphia. Philip, M. S. (1994) Measuring Trees and Forests, 2nd edn. CAB International, Wallingford, UK. Pretty, J. N. (1995) Regenerating Agriculture: Policies and Practices for Sustainability and Self-Reliance. Earthscan Publications, London.

A quantitative

approach for assessing smallholder,fhrms

533

Reijntjes, C., Haverkort, B. and Waters-Bayer, A. (1992) Farming for the Future. An Introduction to Low-External-Input and Suistainable Agriculture. ILEIA/Macmillan. Roger, P. A. (1996) Biology and Management of the Floodwater Ecosystem in Ricejields. International Rice Research Institute, Philippines. Ruddle, K. (1996) The potential of integrated management of natural resources in improving the nutritional and economic status of resource-poor farm households in Ghana. In Research for the Future Development of Aquaculture in Ghana, ed. M. Prein, J. K. Ofori and C. Lightfoot, pp. 57784. ICLARM, Manila. Ruddle, K. and Christensen, V. (1993) An energy flow model of the mulberry dikecarp pond farming system of the Zhujiang Delta, Guangdong Province, China. In Trophic Models of Aquatic Ecosystems, ed. V. Christensen and D. Pauly, pp. 48855. ICLARM, Manila. Ruddle, K. and Prein, M. (1997) Assessing potential nutritional and household economic benefits of developing integrated farming systems. In Integrated Fish Farming. Proceedings of the International Workshop held in Wuxi, Peoples Republic of China, 11 to I5 October 1994. ed. J. Mathias. CRC Press. Rykiel, Jr, E. J. (1984) Modeling Agroecosystems: lessons from ecology. In Agricultural Ecosystems: Untfy~ing Concepts, ed. R. Lowrance, B. R. Stinner and G. J. House, pp. 157-178. John Wiley & Sons, New York. Ruddle, K and Zhong, G. (1983) IntegratedAgriculture-Aquaculture in South China: The Dike-Pond System of the Zhujiang Delta. Cambridge University Press, London. Sanginga, N., Vanlauwe, B. and Danso, S. K. A. (1995) Management of biological N2 fixation in alley cropping systems: estimation and contribution to N balance. Plant Soil 174, 119-141. Smaling, E. M. A. and Fresco, L. 0. (1993) A decision-support model for monitoring nutrient balances under agricultural land use (NUTMON). Geoderma 60,235-256. Soemarwoto, 0. and Conway, G. (1991) The Javanese homegarden. Journal jar Farming Systems Research-Extension 2(3), 95-l 17. Spedding, C. R. W. (1979) An Introduction to Agricultural Systems. Elsevier, London. Stewart, J. L. and Salazar, R. (1992) A review of measurement options for multipurpose trees. Agroforestry Systems 19, 173-l 83. Ulanowicz, R. E. (1986) Growth and Development: Ecosystem Phenomenology. Springer Verlag, New York. van Dam, A. A., Chikafumbwa, F. J. K. T., Jamu, D. M. and Costa-Pierce, B. A. aqua(1993) Trophic interactions in a napier grass (Pennisetum purpureum)-fed culture pond in Malawi. In Trophic Models of Aquatic Ecosystems, ed. V. Christensen and D. Pauly, pp. 65-68. ICLARM, Manila. Villanueva, F. F. D., Lightfoot, C., Gayanilo, Jr, F. C. and McArthur, Jr, H. (in press). Research tools for natural resource management, monitoring and evaluation (RESTORE): software guide. ICLARM Software 9, Vol. 2. International Center for Living Aquatic Resources Management, Manila. Willey, R. W. (ed.) (1981) Proceedings of the International Workshop on Intercropping, IO-13 January 1979, Hyderabad, India. International Center for Research in the Semi-Arid Tropics. Young, A. (1989) Agroforestry for soil conservation. CAB International, Wallingford, UK. Yuan, C. and Leng, H. (1993) A case study of sustainable farming systems, Fuma Site, China. Journal of the Asian Farming Systems Association l(4), 589-601.