On farm testing of integrated nutrient management strategies in eastern Uganda

This paper reports on a Participatory Learning and Action Research (PLAR) process that was initiated in three villages in eastern Uganda in September 1999 to enable small-scale farmers to reverse nutrient depletion of their soils profitably by increasing their capacity to develop, adapt and use integrated natural resource management strategies. The PLAR process was also used to improve the participatory skills and tools of research and extension personnel to support this process. The farming systems of the area were characterised for socio-economic and biophysical conditions that included social organisations, wealth categories, gender, crop, soil, agro forestry and livestock production. Farmers identified soil fertility constraints, their indicators, and causes of soil fertility decline, and suggested strategies to address the problem of soil fertility decline. Soil fertility management diversity among households indicated that most farmers were not carrying out any improved soil fertility management practices, despite previous research and dissemination in the area. Following the diagnosis stage and exposure visits to other farmer groups working on integrated soil fertility projects, the farmers designed 11 experiments for on-farm testing. One hundred and twenty farmers then chose, for participatory technology development, sub-sets of these 11 experiments, based on the major agricultural constraints and the potential solutions identified and prioritised by the farmers. Quantitative and qualitative results from the testing, farmer evaluation and adaptation, training, dissemination strategies and socio-economic implications of these technologies are discussed.     

Economic and environmental evaluation of three goal-vision based scenarios for organic dairy farming in Denmark

The objective of this study was to explore the sustainability of future organic dairy farming systems in Denmark, by evaluating the economic and environmental consequences of three scenarios at the farm level based on different visions of future sustainability leading to different farm-based goals. The future sustainable organic dairy farming systems were deduced from participative sessions with stakeholders, and used to define specific scenarios and related key parameters. Parameterization of the scenarios was based on model simulations and the invoking of expert knowledge. Each scenario was designed to fulfil different aspects of sustainability. The business as usual scenario (BAU) was driven by economic incentives and implemented new technologies and measures to enhance productivity and efficiency. This scenario was expected to be the mainstream strategy of future organic dairy production in Denmark. In the animal welfare scenario (ANW), economic efficiency was subordinate to animal welfare, and measures to improve animal welfare, such as lower milk yield, extra grazing area and a deep-litter barn, were incorporated. The environmental scenario (ENV) was designed to minimize N losses into the environment, reduce emission of greenhouse gases and the use of fossil energy, and was based on self-sufficiency regarding nutrients and feed. The economic evaluation of the scenarios was based on quantification of farm profitability (i.e. net profit), whereas environmental evaluation was based on the quantification of the N-surplus per ha, emission of greenhouse gases, and use of fossil energy per kg energy-corrected milk (ECM).Compared to prolonging the current main stream strategy (BAU), the evaluation of scenarios revealed that investing in animal welfare comprised trade-offs regarding farm profitability, climate change and the use of fossil energy. In ANW, net profit per farm was almost 39 k€ lower than in BAU, whereas emission of greenhouse gases and energy per kg ECM was 8% and 3% higher, respectively. Minimizing environmental impact in ENV reduced local as well as global environmental impact without an economic trade-off. Greenhouse gas emission per kg ECM was 5% lower and fossil energy use was 11% lower than in BAU. The N-surplus of ENV was 80 kg per ha, whereas the N-surplus was approximately 116 in both BAU and ANW. Prolonging the current main stream strategy (BAU) resulted in a high local environmental impact, a moderate global environmental impact and a high economic risk related to changes in milk price or costs.     

Participatory evaluation of integrated pest and soil fertility management options using ordered categorical data analysis

Maize is becoming the major food crop around Lake Victoria. Major constraints to its production are Striga, stem borer, and declining soil fertility. Innovative integrated technologies have been developed: the ‘push-pull’ system (intercropping with Desmodium and surrounded by Napier grass), soybean and Crotalaria rotations, and imidazolinone-resistant (IR) maize seed.In 12 demonstration trials in four villages in Siaya and Vihiga districts (Kenya) and two villages in Busia (Uganda) in 2003 and 2004, 504 farmers evaluated all cropping systems and a mono-cropped continuous maize, each cropped with IR or local maize, and supplemented or not with fertilizer, totaling 16 treatments. Farmers evaluated all treatments for yield, resistance to Striga and stem borer, improvement of soil fertility, and provided an overall evaluation score, using an ordered scale of 1 (very poor) to 5 (very good). Data were analyzed using ordinal regression, estimating log odds ratios.The results show significant preferences for all treatments over the control. Push-pull with IR and fertilizer had the highest log odds ratio (2.93), so the odds of farmers preferring this treatment are 18.7 times the odds that farmers prefer the control. The odds ratios for the other push-pull combinations were generally highest (9-15), followed by the rotation systems with Crotalaria (3.5-7.0), and soybeans, especially with IR maize and fertilizer (odds ratio of 5.7). In mono-cropping systems, IR maize was only appreciated in combination with fertilizer, and then only in 2004. Push-pull and Crotalaria were more appreciated in 2004 than in 2003. Farmers in Vihiga had a stronger preference for push-pull, and those in Busia for soybean rotations.Significant differences among farmers were observed, but the effects were small. Women appreciated push-pull more than men, while other technologies were gender-neutral. Older farmers were more likely to prefer push-pull and Crotalaria with fertilizer. Livestock ownership was not found to have an effect on technology preferences. Measured yield, stem borer and Striga infestation all had significant but small effects, although their inclusion did not eliminate the treatment effects, indicating that other factors are still important. OLS of the scores for different criteria on the overall score shows yield to be the most important criterion (coefficient of 0.40), followed by soil fertility enhancement (0.25) and Striga resistance (0.13). Labor saving (0.09) and stem borer resistance (0.03) are relatively minor criteria.This research shows that scoring and analysis with ordinal regression is a convenient way to solicit and analyze farmers’ preferences for new technologies, with wide applicability in farming systems and participatory research. Its application here shows that farmers like the new technologies, especially push-pull, but that there are substantial differences between years, sites and farmers. The use of this method can be very helpful to define and focus further research and formulate specific and targeted recommendations for agricultural extension.     

Adaptation and evaluation of the integrated farm system model to simulate temperate multiple-species pastures

Whole-farm simulation provides a tool for predicting the effects of farm management strategies on farm productivity and profitability. One such model, the Integrated Farm System Model (IFSM), was modified to allow representation of up to four forage species coexisting in a pasture mixture. The model was calibrated to simulate net herbage accumulation (NHA) observed during six periods of a 2002 experiment in a 3-species pasture in Pennsylvania, USA, composed of orchardgrass (Dactylis glomerata L.), white clover (Trifolium repens L.), and chicory (Cichorium intybus L.). The model also predicted sward botanical composition, total annual NHA, crude protein (CP), and neutral detergent fiber (NDF). Sensitivity analysis showed that predictions of NHA were most sensitive to both chicory and orchardgrass specific leaf area and partitioning of photosynthate to the shoot, as well as chicory photosynthetic efficiency. The model was evaluated against data from the same 3-species pasture in 2003 as well as a 2-species pasture (lacking chicory) from the same experiment in 2002 and 2003. Predictions of total annual NHA in 2- and 3-species pastures were within ±18% of observed values, though predictions of within-season NHA were less accurate. Predictions of botanical composition tended to remain within ±15% of observed values by species. Predictions of within-season CP and NDF concentrations in the whole sward tended to remain within ±22% and ±15%, respectively. Given the generality and realism required of IFSM, the degree of precision in the modified pasture submodel is acceptable for achieving IFSM’s primary goal of comparing the effects of different management scenarios on forage productivity and the long-term profitability and environmental impact of farms.     

Bio-economic evaluation of farmers’ perceptions of viable farms in western Kenya

Arable land in western Kenya is under considerable pressure from increasing human population. Rural households depend on farming for at least part of their livelihood, and poverty rates are among the highest in Kenya. Land is often depleted of nutrients, and for most farmers, access to inputs and markets is poor. There is a need to identify options that are manageable within the context of the farmer’s resource base and the household’s objectives that could improve farm household well-being. In this study we integrated qualitative informal participatory approaches with quantitative mathematical programming and biophysical simulation modelling. Households in four sub-locations in Vihiga District were clustered and pilot cases identified. Meetings were held with farmers to elicit their perceptions of what their ideal farm would look like, and how its performance might compare with their own farm’s performance. With farmers’ help, a range of scenarios was analysed, relating to changes in current enterprise mixes, changes in current farm sizes, and changes in prices of staples foods and cash crops. A considerable mismatch was found between farmers’ estimates of their own farm’s performance, and what was actually produced. There seems to be a threshold in farm size of 0.4 ha, below which it is very difficult for households to satisfy their income and food security objectives. Even for larger farms whose households are largely dependent on agriculture, the importance of a cash crop in the system is critical. There is a crucial role for extension services in making farmers aware of the potential impacts on farm revenue of modest changes in their farm management systems. We are monitoring nine households in the district, whose farmers have made some changes to their system in an attempt to increase household income and enhance food security.     

FARMFLOW—A dynamic model for phosphorus mass flow,simulating conventional and organic management of a Swedish dairy farm

A farm systems mass-balance calculation model, FARMFLOW, was applied to a case study comparing organic and conventional management of a Swedish experimental dairy farm, Öjebyn. Parameterisation of the model is implemented using detailed field data from the Öjebyn farm where the two management systems have been run parallel for more than 11 years. Simulations were made to compare the stocks, flows and resulting balances of phosphorus (P) in the two systems during six crop rotations (36 years). In addition, a maximum animal density scenario was tested, in order to analyse the effects of increased production intensity. Results show that FARMFLOW can be a useful tool for analysing the impact of management on internal farm P dynamics, as well as imports and exports. The organic management results in a higher proportion of internal P flows whereas the conventional system relies more on imports of P in feed and mineral fertilisers. In both management systems, the crop rotation cause large temporal and spatial variation in the application of manure P to the soil system. The resulting field specific soil P accumulation can indicate which fields to target with changed fertilisation management. In the maximum animal density scenario, both management systems led to an application rate of manure P in excess of crop demands.     

Integration of bio-physical and economic models to analyze management intensity and landscape structure effects at farm and landscape level

We present an integrated spatially explicit land use modeling framework, which integrates two key components of agricultural systems, the bio-physical production system and the management system, by coupling the bio-economic farm optimization model FAMOS[space], the crop rotation model CropRota, and the bio-physical process model EPIC (Environmental Policy Integrated Climate). The integrated modeling framework has been developed to analyze the cost-effectiveness of selected agri-environmental program (AEP) measures. We also focus on the landscape development and therefore include a detailed representation of landscape elements such as fields or orchard meadows in our analysis. An indicator set represents all main environmental AEP objectives, i.e. preservation of water and soil resources, mitigation of climate change, protection of biodiversity, maintenance of cultural landscapes as well as farm income support. The integrated modeling framework is applied to 20 farms in the Austrian ‘Mostviertel’ region, which are selected from the Integrated Administration and Control System (IACS) of the European Union. The cost-effectiveness of AEP measures is assessed under different premium levels. The implementation of the AEP clearly affects environmental quality in a positive way. Nitrogen rates are reduced, landscape elements can be sustained, and the landscape becomes more diverse. The program also increases farm gross margins on average. However, the cost-effectiveness ratios (CER) are declining with increasing premium levels. The results indicate that the cost-effectiveness of AEP measures can be improved by spatial targeting.     

The impact of water and agriculture policy scenarios on irrigated farming systems in Italy: An analysis based on farm level multi-attribute linear programming models

The objective of this paper is to evaluate the impacts of agriculture and water policy scenarios on the sustainability of selected irrigated farming systems in Italy, in the context of the forthcoming implementation of the directive EC 60/2000. Directive EC 60/2000 (Water Framework Directive) is intended to represent the reference norm regulating water use throughout Europe. Five main scenarios were developed reflecting aspects of agricultural policy, markets and technologies: Agenda 2000, world market, global sustainability, provincial agriculture and local community. These were combined with two water price levels, representing stylised scenarios for water policy. The effects of the scenarios on irrigated systems were simulated using multi-attribute linear programming models representing the reactions of the farms to external variables defined by each scenario. The output of the models consists of economic, social and environmental indicators aimed at quantifying the impact of the scenarios on different aspects of sustainability relevant for irrigated farming systems. Five Italian irrigated farming systems were considered: cereal, rice, fruit, vegetables and citrus. The results show the diversity of irrigated systems and the different effects that water pricing policy may produce depending on the agricultural policy, market and technological scenarios. They also highlight a clear trade-off between socio-economic sustainability and environmental (water, nitrogen, pesticide) sustainability. Water pricing will have, in most cases, less impact than agricultural markets and policy scenarios, though it appears to be an effective instrument for water regulation in the least intensive irrigated systems considered. This emphasises the need for a differentiated application of the Water Framework Directive at the local level as well as a more careful balance of water conservation, agricultural policy and rural development objectives.     

Identifying and controlling critical sources of farm phosphorus imbalances for Vermont dairy farms

Lake Champlain, located between Vermont, New York, and Quebec exhibits eutrophication mainly due to continuing phosphorus (P) losses from upstream nonpoint source areas. Several state and local agencies have initiated efforts aimed at assessing and identifying critical sources areas for P loss. To augment these efforts, accounting of farm P inputs (in purchased animal feed and fertilizers) and P outputs (in milk, meat, or off-farm sales of harvested crops or other products) is needed as a means of determining potential P build-up in farm soils. When farm P inputs exceed P outputs, P surplus occurs on the farm. This leads to potential soil-P accumulations and risk of P loss in runoff, negatively impacting the quality of receiving water bodies. In this study, a combination of farm record data and a model-based approach, using the Integrated Farming System Model (IFSM), was used to estimate farm P inputs and outputs, identify root causes of farm P imbalances, and explore viable P balancing strategies. Three Vermont dairy farms with varying farm systems (grass-based organic farm, fully confined farm, and a mixed system farm with high-producing confined dairy cows and grazing heifers) were studied. These farms were found to have P surpluses ranging from 5.5 kg/ha to 18.7 kg/ha on annual basis. This study also identified critical causes of P imbalances for each farm and suggested farm specific alternative strategies needed to address the P imbalances. By balancing farm P inputs and outputs, potential accumulation of soil-P can be prevented. As a result, maximum benefits can be obtained from land treatment measures implemented to control off-field P loss without the additional concern of continuing P build-up that could reduce their effectiveness.     

Performance analysis of pressurized irrigation systems operating on-demand using flow-driven simulation models

On-demand pressurized irrigation systems are designed to deliver water with the flow rate and pressure required by the farm irrigation systems, sprinkling or micro-irrigation, and respecting the time, duration and frequency decided by the farmers. Due to the variation in farm demand along the season and the day, a large spatial and temporal variability of flow regimes occurs in these systems, which may affect the performance of the farm systems and the yields of the irrigated crops. Therefore, there is a need to analyse those systems to identify and solve performance problems. In this research, two simulation models for the analysis of irrigation systems operating on-demand, ICARE and AKLA, are used and compared to assess the hydraulic performance of the irrigation network of the Lucefecit Irrigation System, in Southern Portugal. ICARE assesses the global performance of the irrigation system through the indexed characteristic curves, while AKLA provides for the identification of the relative pressure deficit and reliability at every hydrant. Both models adopt a flow-driven analysis approach, performing the analysis for multiple flow regimes. To support the hydraulic characterization of the system and for calibration of the steady-state hydraulic model, field measurements were performed at selected nodes of the network, including four hydrants. The analysis with ICARE does not provide for a sufficient identification of problems. In fact, poor performance is indicated when a few hydrants operate below the minimum pressure set at design. Differently, the analysis with AKLA, applied at the hydrant level, shows that the performance of the Lucefecit system is generally acceptable. AKLA identifies which hydrants operate below the required pressure and, therefore, allows to support any eventual related improvement. Results show that the performance of the system highly improved when changing the piezometric elevation from 260 to 265 m a.s.l. However, this improvement is not sufficient because three hydrants still have high relative pressure deficit and low reliability. Solutions for those hydrants require increasing diameters of network pipes supplying them.     

The environmental performance of milk production on a typical Portuguese dairy farm

The activities associated with raw milk production on dairy farms require an effective evaluation of their environmental impact. The present study evaluates the global environmental impacts associated with milk production on dairy farms in Portugal and identifies the processes that have the greatest environmental impact by using life cycle assessment (LCA) methodology. The main factors involved in milk production were included, namely: the dairy farm, maize silage, ryegrass silage, straw, concentrates, diesel and electricity. The results suggest that the major source of air and water emissions in the life cycle of milk is the production of concentrates. The activities carried out on dairy farms were the major source of nitrous oxides (from fuel combustion), ammonia, and methane (from manure management and enteric fermentation). Nevertheless, dairy farm activities, which include manure management, enteric fermentation and diesel consumption, make the greatest contributions to the categories of impact considered, with the exception of the abiotic depletion category, contributing to over 70% of the total global warming potential (1021.3 kg CO₂ eq. per tonne of milk), 84% of the total photochemical oxidation potential (0.2 kg C₂H₄ eq. per tonne of milk), 70% of the total acidification potential (20.4 kg SO₂ eq. per tonne of milk), and 41% of the total eutrophication potential (7.1 kg eq. per tonne of milk). The production of concentrates and maize silage are the major contributors to the abiotic depletion category, accounting for 35% and 28%, respectively, of the overall abiotic depletion potential (1.4 Sb eq. per tonne of milk). Based on this LCA case study, we recommend further work to evaluate some possible opportunities to improve the environmental performance of Portuguese milk production, namely: (i) implementing integrated solutions for manure recovery/treatment (e.g. anaerobic digestion) before its application to the soil as organic fertiliser during maize and ryegrass production; (ii) improving manure nutrient use efficiency in order to decrease the importation of nutrients; (iii) diversifying feeding crops, as the dependence on two annual forage crops is expected to lead to excessive soil mobilisation (and related impacts) and to insignificant carbon dioxide sequestration from the atmosphere; and (iv) changing the concentrate mixtures.     

Integrating satellite-based evapotranspiration with simulation models for irrigation management at the scheme level

Improvements in irrigation management are urgently needed in regions where water resources for irrigation are being depleted. This paper combines a water balance model with satellite-based remote-sensing estimates of evapotranspiration (ET) to provide accurate irrigation scheduling guidelines for individual fields. The satellite-derived ET was used in the daily soil water balance model to improve accuracy of field-by-field ET demands and subsequent field-scale irrigation schedules. The combination of satellite-based ET with daily soil water balance incorporates the advantages of satellite remote-sensing and daily calculation time steps, namely, high spatial resolution and high temporal resolution. The procedure was applied to Genil–Cabra Irrigation Scheme of Spain, where irrigation water supply is often limited by regional drought. Compared with traditional applications of water balance models (i.e. without the satellite-based ET), the combined procedure provided significant improvements in irrigation schedules for both the average condition and when considering field-to-field variability. A 24% reduction in application of water was estimated for cotton if the improved irrigation schedules were followed. Irrigation efficiency calculated using satellite-based ET and actual applied irrigation water helped to identify specific agricultural fields experiencing problems in water management, as well as to estimate general irrigation efficiencies of the scheme by irrigation and crop type. Estimation of field irrigation efficiency ranged from 0.72 for cotton to 0.90 for sugar beet.     

Simulating field-scale nitrogen management scenarios involving fertiliser and slurry applications

The long-term effects of nitrogen (N) fertiliser and slurry management practices in agricultural systems has been simulated using event driven physically based models. The Swedish soil water model SOIL and its associated nitrogen cycle model SOILN has been used to simulate the long-term impacts (over 12 years) of 360 management scenarios; three slurry applications with 10 spreading dates (involving single and split applications) for surface spreading and injection of slurry, and three fertiliser applications with two spreading dates. The effects of the N management scenarios on NO₃–N drainage flows, total gaseous N losses and crop yields for grass, winter and spring cereals is investigated. Furthermore, seven soils with varying degrees of drainage efficiency and three climatic conditions (East and West coast Scotland and Southern Ireland) are studied.The aim of this work is to produce N-budget tables for an expert agricultural decision system (ADS) which deals specifically with N best management practises for fertiliser and slurry applications. Simulations conducted in this study were based on input parameters calibrated for specific sites in previous studies on hydrology and NO₃–N transport to subsurface drains with associated crop growth.The results of this study show that increasing rates of N applications (in the form of slurry and fertiliser) resulted in a non-linear increase in both the N leached through subsurface drains and the N harvest yield. Surface spreading and injection of slurry gave similar trends. The most important decision about slurry spreading concerns the selection of spreading date and the selection of fields which are likely to produce only moderate leaching effects. Application of slurry in autumn (as a single or split loading), invariably leads to large losses through N leaching, with a single application always resulting in the highest loss. Significant differences are evident for N leaching from the seven soil types. Climatic variation as exemplified in the three meteorological data sets, produces noticeable and significant differences in both N leached and harvest crop totals. This study also aims to identify that a field environmental risk assessment (ERA) using a physically based model such as SOILN can be determined such that strategic agronomic decisions involving N management can be made. In practice this is so provided that a farm manager can recognise and match the actual soil type and drainage condition of the fields on which spreading is to occur with the simulated field types within a similar climate region.     

农机流通品牌经营的发展思路

纵观人类经济活动的历史,一个企业从求生存到谋发展,直至做成大型企业或者集团公司,其整个过程无不显现出在行业中的品牌效应,从而赢得客户、赢得市场、获得成功。21世纪是品牌经济时代,无论国家、地区、企业、服务等,要想在这个知识经济和商业化时代发展,没有正确的品牌战略,不具有强势品牌,企业难以基业常青。     

Strategy factors as drivers and restraints on dairy farm performance: Evidence from Sweden

This paper explored how factors that are potential driving and restraining forces on farm strategy affect farm performance, in terms of economic, technical and allocative input and output efficiency. By assuming some inputs as given in the short run (capital and farmer labor) both long and short run input efficiency scores were calculated. A model of how strategy driving and restraining forces affect farm performance was developed. Three levels of potential driving and restraining forces were identified: external-operational environment, internal environment and micro-social environment. The model was detailed with specific factors, and those of especially the last two types of environments were used as explanatory variables in Tobit regressions, where the dependent variables were data envelopment analysis efficiency scores. We found that several of the strategy factors were important for farm performance. For example, several geographic locations (external-internal environment) were driving forces for economic and technical efficiency. Differences in soil and climate may be reasons, as well as potential differences in business culture. When the internal environment was considered, size of fields, distance to fields, bunker silo and tower silo were found to be driving forces for farm performance, whereas quality of forage machinery and a high focus on dairy production were found to be restraining forces. In the micro-social environment, discussing dairy production with someone was a driving force whereas co-farmers were restraints.     

Model-based evaluation of low-cost drip-irrigation systems and management strategies using saline water

A drip-irrigation module was developed and included in an ecosystem model and tested on two independent datasets, spring and autumn, on field-grown tomato. Simulated soil evaporation correlated well with measurements for spring (2.62 mm d⁻¹ compared to 2.60 mm d⁻¹). Changes in soil water content were less well portrayed by the model (spring r ² = 0.27; autumn r ² = 0.45). More independent data is needed for further model testing in combination with developments of the spatial representation of below-ground variables. In a fresh-water drip-irrigated system, about 30% of the incoming water was transpired, 40% was lost as non-productive evaporative flows, and the remainder left the system as surface runoff or drainage. Simulations showed that saline water irrigation (6 dS m⁻¹) caused reduced transpiration, which led to higher drainage and soil evaporation, compared with fresh water. Covering the soil with plastic mulch resulted in an increase in yield and transpiration. Finally, two different drip-irrigation discharge rates (0.2 and 2.5 l h⁻¹) were compared; however the simulations indicated that the discharge rate did not have any impact on the partitioning of the incoming water to the system. The model proved to be a useful tool for evaluating the importance of specific management options.

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Development and evaluation of a pastoral simulation model that predicts dairy cattle performance based on animal genotype and environmental sensitivity information

A dairy cattle simulation model for pastoral systems that considers how dairy cow genotypes respond to different environments is described. The dairy cow is represented by five modules for maintenance, pregnancy, growth, body energy reserves and lactation with the influence of environmental factors on processes included within each module. Feed intake is predicted based on the requirements for maintenance, growth and pregnancy, and the dairy cow’s potential for yields of milk, fat and protein and body fat change in a given environment. The effects of various temporary environmental factors such as cow body condition score, climate, feed quality and the stage of pregnancy are all considered when predicting yields of milk, fat and protein, energy and dry matter intake. The model was evaluated using information from a prior experimental study with 1990s Holstein-Friesian dairy cattle of North American/European or New Zealand origin managed in a pasture-based system in early to peak lactation. The model was able to predict, to a high degree of accuracy, mean values for yields of milk, fat and protein, and concentrations of fat and protein. However for individual cows, feed intake and live weight change were less reliably predicted. The major source of error was a lack of simulated variation, rather than any systematic bias. The major advance of the model is its ability to predict performance from genetic and environmental sensitivity information for particular breeds, and its ability to predict feed intake and yields of milk, fat and protein concurrently.     

Economic optimization of dairy heifer management decisions

A farmer exercises control over the heifer rearing unit in two main areas: a nutritional plane of growth and the moment of insemination. The two management controls interact with biological aspects of growth thereby influencing future profitability of the dairy heifer. A dynamic programming model was developed to optimize these decisions for individual heifers, using the hierarchic Markov process (HMP) technique. HMP provides a method to model a wide variety of heifer calves, differing in age, season, body weight, reproductive status and prepubertal growth level. Under Dutch conditions the optimal rearing strategy resulted in an average calving age of 22.6 months at a calving weight of 564 kg. Faced with the scarcity of exact information on the interrelationships of rearing strategies with the productivity of the dairy replacement, the strength of this heifer model lies in the field of the sensitivity analyses by providing valuable information regarding the critical components of heifer rearing.     

Development and evaluation of an integrated simulation model for assessing smallholder crop-livestock production in Yucatán, Mexico

Mixed farming systems constitute a large proportion of agricultural production in the tropics, and provide multiple benefits for the world’s poor. However, our understanding of the functioning of these systems is limited. Modeling offers the best approach to quantify outcomes from many interacting causal variables in these systems. The objective of this study was to develop an integrated crop-livestock model to assess biophysical and economic consequences of farming practices exhibited in sheep systems of Yucatán state, Mexico. A Vensim™ dynamic stock-flow feedback model was developed to integrate scientific and practical knowledge of management, flock dynamics, sheep production, partitioning of nutrients, labor, and economic components. The model accesses sheep production and manure quantity and quality data generated using the Small Ruminant Nutrition System (SRNS), and interfaces on a daily basis with an Agricultural Production Systems Simulator (APSIM) model that simulates weather, crop, and soil dynamics. Model evaluation indicated that the integrated model adequately represents the complex interactions that occur between farmers, crops, and livestock.