No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Concepts in production ecology for analysis and design of animal and plant–animal production systems
Abstract
The use of a hierarchy in growth factors (defining, limiting and reducing growth factors), as developed for plant production has shown its usefulness in the analysis and design of plant production systems. This hierarchy presents a theoretical framework for the analysis of biophysical conditions in plant production. We hypothesize that analysis and design of agricultural land use systems is facilitated by development of a similar set of production ecological concepts for animal production, as livestock is often part of such systems. In this paper we present such a hierarchy. We identify growth defining (temperature, daylength, animal genetic characteristics), limiting (water and feed quantity and quality) and reducing (diseases, pollutants and other conditions leading to sub-optimal wellbeing) factors, determining the production of an individual animal, in parallel to their definition for crop production, and aggregate this production to herd scale. We discuss how management intervenes with these factors. Application of the production ecological concepts in design of land use systems ensures that all production systems are based on the prevailing biophysical characteristics and that intrinsic system properties are taken into account. This approach also provides a valuable framework for yield-gap analysis, explaining current production levels, and identifying constraining factors, for setting the research agenda by identifying knowledge gaps and for educational purposes. We illustrate application of the concepts in the exploration and design of alternative animal and mixed plant–animal production systems with two examples. The first example relates to potential production in intensive dairy farming in a temperate climate and the second to feed-limited cattle production in the tropics.
... The potential production of crops and livestock is obtained under ideal management, and is determined by genotype and climate only. The next level is referred to as limited production, where water or nutrient availability limits crop growth, and where drinking water, feed quality or available feed quantity limits livestock growth (Van de Ven et al., 2003;Van Ittersum et al., 2013;Van der Linden et al., 2015). The actual production is the production level of crops and livestock realised by farmers. ...
... The actual production is the production level of crops and livestock realised by farmers. In addition to the limiting factors, actual crop production can be reduced by pests, diseases and weeds, whereas actual livestock production can be reduced by diseases and stress (Van Ittersum and Rabbinge, 1997; Van de Ven et al., 2003;Van der Linden et al., 2015). The difference between the potential or limited production and the actual production is defined as the yield gap. Quantification of yield gaps thus indicates how much agricultural production can be increased from a biophysical perspective (Lobell et al., 2009, Van Ittersum et al., 2013. ...
... Examples of yield gap analyses using such models include those at the farm level for smallholder dairy farms in Mexico with the model FarmDESIGN (Cortez-Arriola et al., 2014), and at the household level for smallholder dairy farms in Ethiopia and India with the integrated analysis tool (Mayberry et al., 2017). However, to our knowledge, concepts of production ecology ( Van de Ven et al., 2003;Van der Linden et al., 2015) are only included explicitly in the model LIVSIM (LIVestock SIMulator), a model simulating dairy production in smallholder farming systems in sub-Saharan Africa (Rufino et al., 2009). LIVSIM does not include the effects of the defining factor climate and has a rather coarse time step of 30 days. ...
The expected increase in the global demand for livestock products calls for insight in the scope to increase actual production levels across the world. This insight can be obtained by using theoretical concepts of production ecology. These concepts distinguish three production levels for livestock: potential (i.e. theoretical maximum) production, which is defined by genotype and climate only; feed-limited production, which is limited by feed quantity and quality; and actual production. The difference between the potential or limited production and the actual production is the yield gap. The objective of this paper, the first in a series of three, is to present a mechanistic, dynamic model simulating potential and feed-limited production for beef cattle, which can be used to assess yield gaps. A novelty of this model, named LiGAPS-Beef (Livestock simulator for Generic analysis of Animal Production Systems – Beef cattle), is the identification of the defining factors (genotype and climate) and limiting factors (feed quality and available feed quantity) for cattle growth by integrating sub-models on thermoregulation, feed intake and digestion, and energy and protein utilisation. Growth of beef cattle is simulated at the animal and herd level. The model is designed to be applicable to different beef production systems across the world. Main model inputs are breed-specific parameters, daily weather data, information about housing, and data on feed quality and quantity. Main model outputs are live weight gain, feed intake and feed efficiency (FE) at the animal and herd level. Here, the model is presented, and its use is illustrated for Charolais and Brahman × Shorthorn cattle in France and Australia. Potential and feed-limited production were assessed successfully, and we show that FE of herds is highest for breeds most adapted to the local climate conditions. LiGAPS-Beef also identified the factors that define and limit growth and production of cattle. Hence, we argue the model has scope to be used as a tool for the assessment and analysis of yield gaps in beef production systems.
... Although concepts of production ecology were initially applied in crop sciences only, they have recently been extended to the livestock sciences (Van de Ven et al., 2003;Van der Linden et al., 2015). This led to the development of LiGAPS-Beef (Livestock simulator for Generic analysis of Animal Production Systems -Beef cattle), a mechanistic model simulating potential and feed-limited growth of beef cattle. ...
... Limited livestock production is determined by feed quality and the quantity of available feed. Farm management is ideal under potential and limited livestock production ( Van de Ven et al., 2003;Van der Linden et al., 2015). ...
... Crop management is assumed to be ideal under water-limited conditions, except for the supplementation of water (Van Ittersum and Rabbinge, 1997). Likewise, livestock management is assumed to be ideal under feed-limited conditions, except for the supplementation of feed ( Van de Ven et al., 2003;Van der Linden et al., 2015). Ideal management implies that decisions on culling rates, selling or slaughter weights, calving dates, age at first calving, and stocking densities are optimized for maximum LW production per hectare ( Table 2). ...
Sustainable intensification is a strategy contributing to global food security. The scope for sustainable intensification in crop sciences can be assessed through yield gap analysis, using crop growth models based on concepts of production ecology. Recently, an analogous cattle production model named LiGAPS-Beef (Livestock simulator for Generic analysis of Animal Production Systems – Beef cattle) was developed, which allows yield gap analysis in beef production systems. This paper is the first to assess yield gaps of integrated feed-crop livestock systems, to analyse underlying causes of yield gaps, and to identify feasible improvement options. We used grass-based beef production in the Charolais area of France as a case study. To this end, we combined LiGAPS-Beef with crop growth models that simulate grass production (fresh grass under grazing, grass silage, hay) and wheat production (concentrate). Feed crop and cattle production were integrated to simulate potential and resource-limited live weight (LW) production per hectare. Potential production is defined as the theoretical maximum LW production per ha, in the absence of resource or management limitations. Resource-limited production is determined by availability of one or several resources: water and nutrients for crops, and feed quality and quantity for animals. Potential production of a cattle herd with an ad libitum diet of grass silage was 2380 kg LW ha− 1 year− 1 and resource-limited production was 664 kg LW ha− 1 year− 1. Actual LW production (354 kg LW ha− 1 year− 1) was 15% of the potential production, implying a relative yield gap of 85%, and 53% of the resource-limited production, implying a relative yield gap of 47%. Applying yield gap analysis disentangled the major biophysical causes of these yield gaps: feeding diets other than the ad libitum grass silage diet, water-limitation in feed crops, and sub-optimal management. These yield gaps suggest scope to intensify beef production. We demonstrate, however, that yield gap mitigation decreased the operational profit per kg LW under the European regulations for bovine and grassland premiums operational in 2014. Hence, as expected, the premiums aiming to support farmers' income and to promote sustainable agriculture and rural development were not conducive to narrow yield gaps at the same time. The current common agricultural policy (CAP, 2015–2020) provides more scope for intensification, such as increasing stocking density via better grassland management.
... Increasing production per animal can be associated with negative effects on animal welfare, which stresses the need of an ethical framework that disbars some options for livestock production (Garnett et al., 2013). Hence, putting an improvement into practice requires both yield gap analysis and analysis of specific non-biophysical constraints (Oosting et al., 2014;Van de Ven et al., 2003;Van Ittersum et al., 2013). ...
... Although yield gap analysis is commonly applied to cropping systems, it is not applied to livestock systems, to our knowledge. Van de Ven et al. (2003) already demonstrated that a similar set of production ecological concepts used in crop production can be used also in livestock production. They broadly quantified potential and limited levels of livestock production, but separate effects of genotype, climate, feed quantity, and feed quality on production were not quantified. ...
... Analogous to the production ecological concepts used in crop production ( Fig. 1A) (Lobell et al., 2009;Van Ittersum et al., 2013;Van Ittersum and Rabbinge, 1997), Van de Ven et al. (2003) identified growth defining, limiting, and reducing factors in livestock production (Fig. 1B). Growth defining factors in animal production are animal genotype, also referred to as animal breed, and climate. ...
In crop science, widely used theoretical concepts of production ecology comprise a hierarchy in growth defining, limiting, and reducing factors, which determine corresponding potential, limited, and actual production levels. These concepts give insight in theoretically achievable production, yield gaps, and yield gap mitigation. Concepts of production ecology have been demonstrated to be applicable to livestock science, but so far they have not been used quantitatively for livestock production. This paper aims to define theoretical concepts of production ecology for livestock systems in more detail, to express livestock production in suitable units, and to provide a framework to analyse production levels for livestock systems and combined crop–livestock systems.
... Ayantunde (1994) stated that studying small ruminant production systems via livestock onfarm studies (farm surveys and on-farm research) often face the problems of experimental design and data analysis, thus a simulation model is helpful to address these problems. To gain better understanding of the interventions for the improvement of small-ruminant production, various simulation models have been developed, such as the Texas A&M Sheep simulation model by ; The the bio-economic model of small ruminant production (Gutierrez-Aleman et al. 1986); the sheep management model by Benjamin (1983); the PCHerd by Brouwer (1992); and LIVSIM (LIVestock SIMulator), a dynamic model based on the principles of production ecology ( Van de Ven et al. 2003). The first requirement of a model for such a system is that it can simulate the effect of nutrition and management on production traits and, hence, productivity. ...
... Model description LIVSIM, the model used in this study, is a dynamic model based on principles of production ecology ( Van de Ven et al. 2003). Following these principles, LIVSIM simulates the performance of individual animals in time, according to their genetic potential and feeding. ...
... Metabolisable energy (ME) and metabolisable protein (MP) requirements are calculated separately for maintenance, growth, pregnancy and lactation. This structure allows application of the concepts of production ecology ( Van de Ven et al. 2003). ...
... Agriculture in China is characterized by large environmental diversity, and large diversity in agricultural products. Promotion of livestock production, mining and forestry in Amazon Trade liberalization, from import competing to export oriented PNPB The Qinling mountains divide China into water-defi cit (North, West) and water-surplus regions (South, Northeast) (Veeck et al. 2011 ). There are four major farming systems (Dixon et al. 2001 ). ...
... In local suitable areas, farmers apply irrigated crop production (e.g., cotton, barley, wheat). Characteristic for the agricultural sector of China are the majority of small-scale farms and the application of multiple cropping systems, that is, the production of more than one crop per year on the same land (Veeck et al. 2011 ). China aims to be largely self-suffi cient in grain production. ...
... China aims to be largely self-suffi cient in grain production. Because of its large population, land availability for agricultural production is an important issue (Veeck et al. 2011 ). Economic growth has caused a major increase in population and demand for housing, transport, and industry in the Eastern coastal area. ...
The sustainable production potential of biomass for energy and material purposes largely depends on the future availability of surplus agricultural lands made available through yield improvements in crop and livestock production. However, the rates at which yields may develop, and the influence of technological, economic and institutional factors on these growth rates are key uncertainties in assessing the potentials and impacts of biomass production. This study analyzes the pace and direction of historical yield developments (1961–2010) of five major crops, beef and cow milk in Australia, Brazil, China, India, USA, Zambia, and Zimbabwe, and examines the driving factors behind these developments. In addition, it explores how future yields are modeled and how modeling efforts may be improved. Average yield growth rates over the investigated period ranged in most cases between 0.7–1.6% year−1 for crops, 1.0–1.5% year−1 for milk, and 0.4–0.8% year−1 for beef (relative to 2010). The role of different drivers is region specific. Yet, supporting agricultural policies have played an important role in increasing yields in all countries, especially for crops. In cattle production, a key factor was the importance of commercial beef and milk production for the national or export market. Based on regional differences in drivers and yield developments, models that assess biomass potentials and impacts should take into account regional drivers, yield gaps, and potential policy pathways.
... Agricultural production systems are a function of both biophysical and socio-economic conditions (Van de Ven et al., 2003). Producers are increasingly expected to apply production methods that will ensure long-term sustainability of natural resource use and thus also of farming enterprises. ...
... Before discussing the model itself, it is necessary to highlight the underlying principles on which an animal production system and thus also the decision making model are based. Van de Ven et al. (2003) identified three basic production situations for an individual animal, namely, potential, limited and reduced production. According to Van de Ven et al. (2003) potential production only occurs when all the water and feed requirements (limiting factors) of an animal have been met in the absence of reducing factors such as disease and pollutants. ...
... Van de Ven et al. (2003) identified three basic production situations for an individual animal, namely, potential, limited and reduced production. According to Van de Ven et al. (2003) potential production only occurs when all the water and feed requirements (limiting factors) of an animal have been met in the absence of reducing factors such as disease and pollutants. ...
Agricultural activities are depicted as highly dependent on weather conditions. The need to employ risk management strategies is emphasized leading to the aim of the paper stating that a basic qualitative decision making model should be adopted to ensure timely adjustments to weather conditions. Factors affecting production are discussed. Two models adapted to an arid environment as well as their practical application in an actual production scenario are discussed. It is concluded that the incorporation of pro-active decision making models into the management plan contributes towards the stabilization of the production system, while also protecting the grazing resources.
... These crop growth models are often developed using field and experimental data, thus providing reliable scientific estimates of plant growth and potential yields. Similar ecological production concepts also have been applied to develop models that estimate potential growth for animals (van de Ven et al., 2003). These growth models are a useful tool when designing agricultural systems for the maximisation of production outputs (de Koeijer et al., 1999;van Ittersum and Rabbinge, 1997). ...
... For individual animals, analogous to crops, production factors can be classified into growthdefining, growth-limiting and growth-reducing factors (van de Ven et al., 2003). The growthdefining factors of animals comprise climate conditions (mainly temperature and day length) and animal genetic characteristics, including sex. ...
... In agronomy, identification of optimal combination of inputs to realise a particular output level is referred to as a target-oriented or engineering approach in which inputs are quantified, based on agronomic knowledge of the physical, chemical, physiological and ecological process involved in crop and animal growth (van de Ven et al., 2003;van Ittersum and Rabbinge, 1997). Also, based on this knowledge, for each combination of physical environment and type of crops and animals, biophysical production possibilities can be estimated. ...
This article presents a new two-stage analytical framework to analyse the productive efficiency of crop production systems. In the first stage, crop growth and economic production models are estimated to calculate three measures of productive efficiency: (1) agronomic efficiency, as the ratio of actual yield to potential yield; (2) technical efficiency (TE), as the ratio of actual yield to best practice yield; and (3) agro-economic efficiency (AgEcE), as the ratio of best practice yield to potential yield. In the second stage, TE and AgEcE are analysed in relation to economic, institutional, social and technological factors that cause farm and spatial heterogeneity. The framework was illustrated through an empirical analysis of rice production in Sri Lanka.
... Feed quality and quantity are major aspects determining the production and productivity of chickens. The theoretical concepts of production ecology have been applied in assessing the potential, limited, and actual crop and animal production ( Van de Ven et al., 2003;van der Linden et al., 2015). When applied to animal production, potential production is defined by the growth-defining factors, that is, genotype and climate, while limited production is defined by the growth-limiting factors, that is, feed quantity and nutritional quality (van der Linden et al., 2015). ...
... In the next step, we analyzed the effects of the rearing systems and chicken breed and their interaction effect on feed quantity and quality. To the best of our knowledge, there are no relevant simulation models for estimating the potential production of chickens which would be needed to estimate the yield gap (Van de Ven et al., 2003;van Ittersum et al., 2013;van der Linden et al., 2021). Considering the crucial importance of feed in closing the yield gap in chicken production, we focused on the yield that could be attained by closing feed gaps (both quantity and quality). ...
... This integration requires a hierarchy in growth factors. A framework that presents such a hierarchy in biophysical factors is referred to as the concepts of production ecology (Van de Ven et al., 2003;Van der Linden et al., 2015). This framework distinguishes potential, limited and actual production of livestock. ...
... Limited production occurs when the defining factors such as genotype and climate as well as the limiting factors which include feed quality, available feed quantity and drinking water affect livestock production. The difference between limited production and actual production is caused by the reducing factors i.e. diseases, and stress ( Van de Ven et al., 2003;Van der Linden et al., 2015). ...
... Feed quality and quantity are major aspects determining the production and productivity of chickens. The theoretical concepts of production ecology have been applied in assessing the potential, limited, and actual crop and animal production ( Van de Ven et al., 2003;van der Linden et al., 2015). When applied to animal production, potential production is defined by the growth-defining factors, that is, genotype and climate, while limited production is defined by the growth-limiting factors, that is, feed quantity and nutritional quality (van der Linden et al., 2015). ...
... In the next step, we analyzed the effects of the rearing systems and chicken breed and their interaction effect on feed quantity and quality. To the best of our knowledge, there are no relevant simulation models for estimating the potential production of chickens which would be needed to estimate the yield gap (Van de Ven et al., 2003;van Ittersum et al., 2013;van der Linden et al., 2021). Considering the crucial importance of feed in closing the yield gap in chicken production, we focused on the yield that could be attained by closing feed gaps (both quantity and quality). ...
The demand for chicken meat and eggs exceeds what can be produced in Tanzania, largely due to low productivity of the sector. Feed quantity and quality are the major factors determining the potential production and productivity of chickens. The present study explored the yield gap in chicken production in Tanzania and analyses the potential of increased chicken production as a result of closing the feed gaps. The study focused on feed aspects limiting dual-purpose chicken production in semi-intensive and intensive systems. A total of 101 farmers were interviewed using a semistructured questionnaire and the amount of feed provided to chickens per day was quantified. Feed was sampled for laboratory analysis and physical assessments were made of weights of chicken bodies and eggs. The results were compared with the recommendations for improved dual-purpose crossbred chickens, exotic layers, and broilers. The results show that the feeds were offered in insufficient quantity compared with the recommendations for laying hens (125 g/chicken unit/d). Indigenous chickens were fed 111 and 67 while the improved crossbred chickens were fed 118 and 119 g/chicken unit/d under semi-intensive and intensive systems, respectively. Most feeds fed to dual-purpose chickens were of low nutritional quality, particularly lacking in crude protein and essential amino acids in both rearing systems and breeds. Maize bran, sunflower seedcake, and fishmeal were the main sources of energy and protein in the study area. The study findings show that the important feed ingredients: protein sources, essential amino acids, and premixes were expensive, and were not included in formulating compound feeds by most chicken farmers. Of all 101 respondents interviewed, only one was aware of aflatoxin contamination and its effects on animal and human health. All feed samples contained a detectable concentration of aflatoxins and 16% of them exceeded the allowed toxicity thresholds (>20 µg/kg). We highlight the need for a stronger focus on feeding strategies and ensuring the availability of suitable and safe feed formulations.
... Considering that there are several kinds of models to predict the productivity of farming systems (potential, limited, and reduced productions; van de Ven et al. 2003) it is important to determine how all of the factors will be used in the determination. These factors can be divided into genetic characteristics, light, and temperature (used for potential productivity models); these last three as well as the water and available nutrients (used for several productivity models) and all of the above-mentioned factors in addition to the presence of weeds and disease (used for reduced productivity models). ...
... Our results were in agreement with those reported by Pasini (2015), who reported that ANNs are chosen based on their ability to find nonlinear realistic relationships between causes and indices which can explain the behaviors of complex systems, such as the biological systems evaluated in this research. Considering the study by van de Ven et al. (2003), the models that were evaluated in this research (forage and animal production) could be considered limited production because it employed information in the inputs, such as the genetic characteristics (crops, breed), climate (temperature, photoperiod, rainfall, rainy days, humidity), as well as water and feed quantity (nitrogen fertilizer level, supplementation level, sward height, forage mass, leaf and stem percentages). ...
This study focuses on training the mathematical models for prediction of forage and animal production in Brazilian beef cattle system. In this study, two functions are trained to find the most optimal prediction of herbage mass besides leaf and stem percentages, and average daily gain. We aimed to compare artificial neural networks (ANNs) and multiple linear regression (MLR) to predict both forage and animal production. Two datasets were used in each evaluation. The multivariable results showed that there was no formation of groups in each dataset, so all inputs were used in analyses. These analyses to determine the best model ANN or MLR results, considering the correlation between the predicted value and the observed value. Other evaluations were performed for ANNs, more specifically for structures. The inputs and the number of hidden layers was analyzed to define the best structure for prediction of future results. Significance level was considered by P-value < 0.05. It was found that ANN is better than MLR predicting results for both data-sets. For the inputs used in each ANN, there were differences only for animal production, with the higher prediction values 0.72 using ANN. In other words, the number of hidden layers for both datasets were not different. Hence, ANN, with a specific structure for each evaluation, is a potential tool for prediction of results for forage and animal production.
... In summary, although using the above representation strategy does not guarantee meaningful clustering it is possible to increase its reliability by permitting a degree of flexibility in its formulation. This flexibility might be achieved by including additional variables according to a hierarchy of agriculture subsystems introduced by Hart (1982) and Van de Ven et al. (2003). ...
... Additionally, it is interesting to note that stocking rate was highest in those farms with more milk production per area, which is consistent with the views of Gillen and Sims (2002). Animal response changes in terms of its productivity, and there are some attributes, such as production per area, that might be positively influenced by stocking rate as a consequence of a non linear relationships (Wilson and Macleod, 1991;Van de Ven et al., 2003). 56 gets more complex. ...
Within the field of pattern recognition (PR) a very active area is the clustering
and classification of multispectral data, which basically aims to allocate the right class of
ground category to a reflectance or radiance signal. Generally, the problem complexity is
related to the incorporation of spatial characteristics that are complementary to the nonlinearities
of land surface process heterogeneity, remote sensing effects and multispectral
features. The present research describes the application of learning machine methods
to accomplish the above task by inducting a relationship between the spectral response
of farms’ land cover, and their farming system typology from a representative set of
instances. Such methodologies are not traditionally used in crop-livestock studies. Nevertheless,
this study shows that its application leads to simple and theoretically robust
classification models. The study has covered the following phases: a)geovisualization of
crop-livestock systems; b)feature extraction of both multispectral and attributive data
and; c)supervised farm classification. The first is a complementary methodology to represent
the spatial feature intensity of farming systems in the geographical space. The
second belongs to the unsupervised learning field, which mainly involves the appropriate
description of input data in a lower dimensional space. The last is a method based on statistical
learning theory, which has been successfully applied to supervised classification
problems and to generate models described by implicit functions.
In this research the performance of various kernel methods applied to the representation
and classification of crop-livestock systems described by multispectral response
is studied and compared. The data from those systems include linear and nonlinearly
separable groups that were labelled using multidimensional attributive data. Geovisualization
findings show the existence of two well-defined farm populations within the
whole study area; and three subgroups in relation to the Guarico section. The existence
of these groups was confirmed by both hierarchical and kernel clustering methods,
and crop-livestock systems instances were segmented and labeled into farm typologies
based on: a)milk and meat production; b)reproductive management; c)stocking rate;
and d)crop-forage-forest land use. The minimum set of labeled examples to properly
train the kernel machine was 20 instances. Models inducted by training data sets using
kernel machines were in general terms better than those from hierarchical clustering
methodologies. However, the size of the training data set represents one of the main
difficulties to be overcome in permitting the more general application of this technique
in farming system studies. These results attain important implications for large scale
monitoring of crop-livestock system; particularly to the establishment of balanced policy
decision, intervention plans formulation, and a proper description of target typologies to
enable investment efforts to be more focused at local issues.
... Os modelos matemáticos desenvolvidos para a previsão da produtividade de sistemas agrícolas são, geralmente, divididos em modelos de produtividade potencial, de produtividade restrita e de produtividade reduzida (VAN DE VEN et al., 2003). Nessa classificação, os modelos de produtividade potencial consideram fatores que definem as máximas taxas de crescimento (características genéticas, luz, temperatura). ...
... A diluição de nitrogênio que, mesmo em condições não limitantes de N, diminui com o crescimento das plantas. Os modelos que contemplam as situações subótimas e mais realísticas são valiosos para o aprimoramento das técnicas de monitoramento e gerenciamento de sistemas pastoris, na identificação de causas de má ou subutilização de recursos (VAN DE VEN et al., 2003 ), no estabelecimento de potenciais regionais de produção de forragem (FITZPATRICK; NIX, 1970), no zoneamento ecológico-econômico e na avaliação de projetos pecuários baseados em pastagens. Infelizmente, os bancos de dados necessários ao desenvolvimento de tais modelos para espécies forrageiras tropicais ainda são restritos, o que faz com que seu desenvolvimento ainda seja incipiente (BARIONI et al., 2003). ...
A demanda crescente pelo desenvolvi-mento de tecnologias voltadas para a explora-ção pecuária é um dos reflexos do processo de globalização e de competição entre os produtos agropecuários. Esses processos tomam propor-ções maiores quando se focam impactos das ati-vidades agropecuárias no meio ambiente e na qualidade e segurança de alimentos oferecidos ao consumidor. Novas exigências impostas pelo mercado brasileiro e internacional têm mudado os rumos da economia e, consequentemente, requerem que os pecuaristas assumam nova postura empresa-rial; no entanto, a gestão de sistemas pastoris defronta-se com a baixa capacidade de previsão da produção e o limitado controle dos estoques de forragem. Na busca pela melhor eficiência econômica dos sistemas produtivos, identificar técnicas de manejo adequadas ao planejamento administrativo permite a tomada de decisões di-recionadas ao melhor rendimento global do sis-tema. Para entender mais profundamente o dinamismo desses sistemas, é necessário o uso de ferramentas que auxiliem os pesquisadores na interpretação dos resultados quando fatores ambientais e de manejo são adicionados ao pro-cesso. Dentre as novas ferramentas, destaca-se o uso de modelos de simulação, os quais permitem descrever o funcionamento de um sistema pro-dutivo e inter-relacionar seus componentes. O uso de modelos na pesquisa auxilia na identificação de lacunas de conhecimento e para fornecer subsídios teóricos para estudos mais complexos. Nas propriedades agrícolas, os modelos auxiliam, entre outras finalidades, para estimar a produção, permitindo maior controle sobre a oferta e a demanda de alimentos e geran-CAPÍTULO do informações importantes ao produtor quanto ao planejamento das atividades (BARIONI et al., 2003). Neste capítulo, são revistos conceitos téc-nicos sobre modelagem relacionada às pastagens e aplicações na pesquisa de modelos de simula-ção para a gestão dos recursos forrageiros. O que é um modelo? A palavra modelo deriva de " modus " (uma medida) e implica mudanças de escala em suas interpretações (ARIS, 1994). De forma geral, um modelo é uma representação de algo físico ou abstrato expresso de outra forma. Sendo assim, a asa de uma aeronave pode ser modelada a partir da construção de um modelo físico (protótipo) e obterem-se medidas a partir dele. Uma entidade química, biológica, social, psicológica, econômica ou conceitual é abstrata, e pode geralmente ser descrita em termos matemáticos (BERNARDES; TERAMOTO, 2007). Um modelo matemático consiste em diversos tipos de expressão mate-mática (como, por exemplo, dinâmica, diferen-cial, probabilística, etc.) que podem representar quantitativamente as suposições e hipóteses ide-alizadas sobre o sistema complexo real (THOR-NLEY,1976). Talvez a mais importante face da mode-lagem seja que ela possibilita entender o sistema de forma mais intensa ou descrevê-lo mais ple-namente, representando quantitativamente as suposições e hipóteses idealizadas sobre o siste-ma real (THORNLEY, 2001). Até mesmo a sequ-ência da análise de um sistema e a concatenação de ideias para o processo de modelagem podem ser simplificadamente descritas por um modelo (Figura 1).
... Figure 1: Concepts in production ecology for analysis and design of animal and plant-animal production system (van de Ven et al., 2003) ...
... Livestock production is simulated by LIVSIM (Appendix III - Fig. 1), currently a running model written in MATLAB 7.0, which is based on production ecological concepts for animal production systems (van de Ven et al., 2003). Different instances of LIVSIM within the farm represent different animal production units: e.g. ...
... Consistent with the 'What if' criterion, all models belonging to the type 'Simulating according to a set of parameters', except one (Van de Ven et al., 2003), are simulation models. Van de Ven's model (2003) model is a conceptual model that uses concepts in production ecology to analyse and design production systems. ...
... In these models, supporting changes are made possible by eliciting some understanding of the system's operation while using a comprehensive viewpoint on the system. These four models belong to the five conceptual models of the whole collection, the fifth being the model of Van de Ven et al. (2003) discussed above. The conceptual structures of these four models themselves make it possible to gain a better understanding of the systems considered. ...
Livestock farming has recently come under close scrutiny, in response especially to environmental issues. Farmers are encouraged to redesign their livestock farming systems in depth to improve their sustainability. Assuming that modelling can be a relevant tool to address such systemic changes, we sought to answer the following question: 'How can livestock farming systems be modelled to help farmers redesign their whole farming systems?' To this end, we made a literature review of the models of livestock farming systems published from 2000 to mid-2009 (n = 79). We used an analysis grid based on three considerations: (i) system definition, (ii) the intended use of the model and (iii) the way in which farmers' decision-making processes were represented and how agricultural experts and farmers were involved in the modelling processes. Consistent rationales in approaches to supporting changes in livestock farming were identified in three different groups of models, covering 83% of the whole set. These could be defined according to (i) the way in which farmers' decisions were represented and (ii) the model's type of contribution to supporting changes. The first type gathered models that dynamically simulated the system according to different management options; the farmers' decision-making processes are assumed to consist in choosing certain values for management factors. Such models allow long-term simulations and endorse different disciplinary viewpoints, but the farmers are weakly involved in their design. Models of the second type can indicate the best combination of farm activities under given constraints, provided the farmers' objectives are profit maximisation. However, when used to support redesigning processes, they address neither how to implement the optimal solution nor its long-term consequences. Models of the third type enable users to dynamically simulate different options for the farming system, the management of which is assumed to be planned according to the farmers' general objectives. Although more comprehensive, these models do not easily integrate different disciplinary viewpoints and different subsystems, which limits their usefulness as support tools for redesigning processes. Finally, we concluded about what specific requirements should be for modelling approaches if farmers were to be supported in redesigning their whole livestock farming systems using models.
... The yield gap designates the gap between what is actually produced and what could be produced under intensive animal husbandry systems and given agro-climatic conditions. (The concept of yield gap is widely used in production ecology and can also be applied to livestock science [see Van de Ven et al., 2003;Van der Linden et al., 2015].) Globally, crop yield gaps are highest in Sub-Saharan Africa, followed by South-East Asia and South America. ...
Non-technical summary
Scenarios compatible with the Paris agreement's temperature goal of 1.5 °C involve carbon dioxide removal measures – measures that actively remove CO 2 from the atmosphere – on a massive scale. Such large-scale implementations raise significant ethical problems. Van Vuuren et al. (2018), as well as the current IPCC scenarios, show that reduction in energy and or food demand could reduce the need for such activities. There is some reluctance to discuss such societal changes. However, we argue that policy measures enabling societal changes are not necessarily ethically problematic. Therefore, they should be discussed alongside techno-optimistic approaches in any kind of discussions about how to respond to climate change.
Technical summary
The 1.5 °C goal has given impetus to carbon dioxide removal (CDR) measures, such as bioenergy combined with carbon capture and storage, or afforestation. However, land-based CDR options compete with food production and biodiversity protection. Van Vuuren et al. (2018) looked at alternative pathways including lifestyle changes, low-population projections, or non-CO 2 greenhouse gas mitigation, to reach the 1.5 °C temperature objective. Underlined by the recently published IPCC AR6 WGIII report, they show that demand-side management measures are likely to reduce the need for CDR. Yet, policy measures entailed in these scenarios could be associated with ethical problems themselves. In this paper, we therefore investigate ethical implications of four alternative pathways as proposed by Van Vuuren et al. (2018). We find that emission reduction options such as lifestyle changes and reducing population, which are typically perceived as ethically problematic, might be less so on further inspection. In contrast, options associated with less societal transformation and more techno-optimistic approaches turn out to be in need of further scrutiny. The vast majority of emission reduction options considered are not intrinsically ethically problematic; rather everything rests on the precise implementation. Explicitly addressing ethical considerations when developing, advancing, and using integrated assessment scenarios could reignite debates about previously overlooked topics and thereby support necessary societal discourse.
Social media summary
Policy measures enabling societal changes are not necessarily as ethically problematic as commonly presumed and reduce the need for large-scale CDR.
... In contrast, process-based models offer a systematic approach to exploring various management scenarios on different spatiotemporal scales and can help identify suitable management strategies once they have been satisfactorily evaluated (Kersebaum et al., 2015). Crop models (CMs) and Livestock Models (LMs) can be used to simulate the combined effects of diverse management strategies on biomass, yield, water use, nutrient uptake (e.g., Rötter and Van Keulen, 1997; Whitbread et al., 2010), and animal productivity, considering genotype, environmental interactions, and various herd characteristics (e.g., age, gender, and status; see van de Ven et al., 2003). As an example of studies utilizing modeling tools to investigate the impact of climate change on forage availability, livestock, and crop productivity, Descheemaeker et al. (2018) have employed the crop model APSIM (Holzworth et al., 2014) and the livestock model Livsim (Ru no et al., 2015) within the AgMIP framework . ...
Climate change signi cantly challenges smallholder mixed crop-livestock (MCL) systems in sub-Saharan Africa (SSA), affecting food and feed production. This study enhances the SIMPLACE modeling framework by incorporating crop-vegetation-livestock models, which contribute to the development of sustainable agricultural practices in response to climate change. Applying such a framework in a domain in West Africa (786,500 km 2) allowed us to estimate the changes in crop (Maize, Millet, and Sorghum) yield, grass biomass, livestock numbers, and greenhouse gas emission in response to future climate scenarios. We demonstrate that this framework accurately estimated the key components of the domain for the past (1981-2005) and enables us to project their future changes using dynamically downscaled Global Circulation Model (GCM) projections (2020-2050). The results demonstrate that in the future, northern part of the study area will experience a signi cant decline in crop biomass (upto-56%) and grass biomass (upto-57%) production leading to a decrease in livestock numbers (upto-43%). Consequently, this will impact total emissions (upto-47% CH 4) and decrease of-41% in milk production,-47% in meat production concentrated in the Sahelian zone. Whereas, in pockets of the Sudanian zone, an increase in livestock population and CH 4 emission of about +24% has been estimated.
... The role of catchments (or watersheds) in providing ecological services to agriculture has often been underrecognised, although there is growing interest among mainstream researchers in evaluating methods of integrating farmland, natural vegetation, water bodies and other landscape features (Stirzaker et al. 2000, van de Ven et al. 2003. In addition to work on the ecological services provided by diverse landscapes (see Chapter 4), research on systems design has investigated perennial systems, farm layout, crop rotations, water management and so on (Doing 1997, Kuiper 1997, Vereijken et al. 1997, see also Chapter 1). ...
This book documents current practices in organic agriculture and evaluates their strengths and weaknesses. All major aspects of organic agriculture are explored including historical background and underlying principles, soil fertility management, crop and animal production, breeding strategies, crop protection, animal health and nutrition, animal welfare and ethics, economics and marketing, standards and certification, environmental impacts and social responsibility, food quality, research, education and extension. The book has 18 chapters and a subject index. A special feature of this book is a series of 5 'Special Topics', smaller sections that address key questions or challenges facing organic agriculture. These sections are intended to provide a more detailed analysis of specific issues that cannot be covered as sufficiently in the larger general chapters.
... Number of studies and observations are reported in brackets impacts from livestock feeding regimes when compared with forage productivity points to the fact that higher quantity and quality of feed does not directly translate into livestock productivity response as several other factors such as current nutritional status of the animals, animal health, breed, and management practices might be limiting productivity. A combination of interventions is often necessary, including improved animal breeds, husbandry, and health to reach desired productivity responses (Van De Ven et al. 2003). ...
Scarcity of quantity and quality feed has been a key constraint to productivity of smallholder crop-livestock systems. Tropical forages include a variety of annual and perennial grasses, herbaceous and dual-purpose legumes, and multipurpose trees and shrubs. They have been promoted in Sub-Saharan Africa (SSA) for increasing livestock productivity and household income through higher quantity and quality of herbage, while contributing to soil improvement and higher food crop yields. For the first time, we quantitatively reviewed 72 experimental studies from across SSA to take stock of geographical distribution and forage technology focus of past research; quantify magnitudes of multidimensional impacts of forage technologies; and present variability in forage agronomy data. Improved forage technologies were classified into four groups: (i) germplasm, (ii) management, (iii) cropping system integration, and (iv) feeding regime. Mean weighted response ratios were calculated from 780 pairs of observations for 13 indicators across the five impact dimensions. Improved forage germplasm had on average 2.6 times higher herbage productivity than local controls, with strongest effect in grasses. Feeding regimes with improved leguminous forages increased milk yield by on average 39%, dry matter intake by 25%, and manure production by 24%. When forage technologies were integrated with food crops, soil loss was almost halved, soil organic carbon increased on average by 10%, and grain and stover yields by 60% and 33%, respectively. This study demonstrates the central role improved forages could play in sustainable intensification of crop-livestock systems in SSA. It highlights the need for multidisciplinary and systems-level approaches and studies to quantify synergies and tradeoffs between impact dimensions. Further research is needed to explain forage agronomic yield variability, unraveling interactions between genotype, on-farm environmental conditions, and management factors. Results from this review can inform development programs, prioritizing technologies proven successful for dissemination and indicating magnitudes of expected impacts.
... where O is the observed value, S the simulated value and n the number of observations. Factors that define growth are cattle genotype, or breed, and climate via heat and cold stress (Van de Ven et al., 2003;Van der Linden et al., 2015). Feed quality and quantity are factors that can limit growth in LiGAPS-Beef due to a lack of digestive capacity, energy deficiency or protein deficiency (Van der Linden et al., 2018b). ...
LiGAPS-Beef (Livestock simulator for Generic analysis of Animal Production Systems – Beef cattle) is a generic, mechanistic model designed to quantify potential and feed-limited growth, which provides insight in the biophysical scope to increase beef production (i.e. yield gap). Furthermore, it enables identification of the bio-physical factors that define and limit growth, which provides insight in management strategies to mitigate yield gaps. The aim of this paper, third in a series of three, is to evaluate the performance of LiGAPS-Beef with independent experimental data. After model calibration, independent data were used from six experiments in Australia, one in Uruguay and one in the Netherlands. Experiments represented three cattle breeds, and a wide range of climates, feeding strategies and cattle growth rates. The mean difference between simulated and measured average daily gains (ADGs) was 137 g/day across all experiments, which equals 20.1% of the measured ADGs. The root mean square error was 170 g/day, which equals 25.0% of the measured ADGs. LiGAPS-Beef successfully simulated the factors that defined and limited growth during the experiments on a daily basis (genotype, heat stress, digestion capacity, energy deficiency and protein deficiency). The simulated factors complied well to the reported occurrence of heat stress, energy deficiency and protein deficiency at specific periods during the experiments. We conclude that the level of accuracy of LiGAPS-Beef is acceptable, and provides a good basis for acquiring insight in the potential and feed-limited production of cattle in different beef production systems across the world. Furthermore, its capacity to identify factors that define or limit growth and production provides scope to use the model for yield gap analysis.
... The biophysical scope to increase livestock production is the difference between the potential (i.e. maximum theoretical) production or feedlimited production and the actual production realized in practice, which is also referred to as the yield gap ( Van de Ven et al., 2003;Van der Linden et al., 2015). Identifying geographical regions with large yield gaps contributes to insight where food production can be increased per unit of land, which is generally regarded as a better strategy than expanding agricultural land at the expense of nature (Lobell et al., 2009;Van Ittersum et al., 2013). ...
The model LiGAPS-Beef (Livestock simulator for Generic analysis of Animal Production Systems – Beef cattle) has been developed to assess potential and feed-limited growth and production of beef cattle in different areas of the world and to identify the processes responsible for the yield gap. Sensitivity analysis and evaluation of model results with experimental data are important steps after model development. The first aim of this paper, therefore, is to identify which parameters affect the output of LiGAPS-Beef most by conducting sensitivity analyses. The second aim is to evaluate the accuracy of the thermoregulation sub-model and the feed intake and digestion sub-model with experimental data. Sensitivity analysis was conducted using a one-at-a-time approach. The upper critical temperature (UCT) simulated with the thermoregulation sub-model was most affected by the body core temperature and parameters affecting latent heat release from the skin. The lower critical temperature (LCT) and UCT were considerably affected by weather variables, especially ambient temperature and wind speed. Sensitivity analysis for the feed intake and digestion sub-model showed that the digested protein per kg feed intake was affected to a larger extent than the metabolisable energy (ME) content. Sensitivity analysis for LiGAPS-Beef was conducted for ¾ Brahman×¼ Shorthorn cattle in Australia and Hereford cattle in Uruguay. Body core temperature, conversion of digestible energy to ME, net energy requirements for maintenance, and several parameters associated with heat release affected feed efficiency at the herd level most. Sensitivity analyses have contributed, therefore, to insight which parameters are to be investigated in more detail when applying LiGAPS-Beef. Model evaluation was conducted by comparing model simulations with independent data from experiments. Measured heat production in experiments corresponded fairly well to the heat production simulated with the thermoregulation sub-model. Measured ME contents from two data sets corresponded well to the ME contents simulated with the feed intake and digestion sub-model. The relative mean absolute errors were 9.3% and 6.4% of the measured ME contents for the two data sets. In conclusion, model evaluation indicates the thermoregulation sub-model can deal with a wide range of weather conditions, and the feed intake and digestion sub-model with a variety of feeds, which corresponds to the aim of LiGAPS-Beef to simulate cattle in different beef production systems across the world.
... In the introduction of this thesis (Figure 1.1), heat stress was classified as a yield-reducing factor, which can be considered an extension of a commonly used classification scheme to distinguish between production conditions and the respective factors to determine, limit and reduce yields of crops ( Van de Ven et al., 2003;Van Ittersum et al., 2013). Considering heat stress a yield-reducing was justified due to its nature to reduce grain number and grain weight as presented in chapter 2. ...
The production of cereal crops is increasingly influenced by heat and drought stress. Despite the typical small-scale sub-regional variability of these stresses, impacts on yields are also of concern at larger regional to global scales. Crop growth models are the most widely used tools for simulating the effects of heat and drought stress on crop yield. However, the development and application of crop models to simulate heat and drought is still a challenging issue, particularly their application at larger spatial scales. Previous research showed that there is a lack of information regarding the:
1. Response of cereal crops to heat stress,
2. Interactions between phenology and heat stress under climate change,
3. Improvement of crop models for reproducing heat stress effects on crop yield,
4. Upscaling of heat and drought stress effects with crop models,
5. Effects of climate and management interactions on crop yield in semi-arid environments.
Five detailed studies were arranged to improve the understanding on the aforementioned gaps of knowledge:
1. A review study was set up to understand how crop growth processes responded to short episodes of high temperature. In addition, the possible ways for improvement of the heat stress simulation algorithms in crop models were investigated at a field scale. The reproductive phase of development in cereals was found to be the most sensitive phase to heat stress. Crop models aiming to model heat stress effects on crops under field conditions should consider the modelling of canopy temperature. This may also provide a mechanistic basis to link heat and drought stress in crop models. Generally, these two stresses occur simultaneously.
2. In a nationwide study, the interactions between the advancements of phenology and heat stress on winter wheat (Triticum aestivum L.) due to global warming, were evaluated between1951-2009 across Germany. The increase in temperature (~1.8°C) shifted crop phenology to cooler parts of the growing season (~14 days) and compensated for the effect of global warming on heat stress intensity in the period 1976-2009. The intensity of heat stress on winter wheat could have increased by up to 59% without any advancement in phenology.
3. A large-scale simulation study was conducted to investigate the effects of input (climate and soil) and output data aggregation on simulated heat and drought stress for winter wheat over the period of 1980-2011 across Germany. Aggregation levels were compared in several steps from 1 km × 1 km to 100 km × 100 km. Simulations were performed with SIMPLACE . Aggregation of weather and soil data showed a slight impact on the mean and median of simulated heat and drought stress at the national scale. No remarkable differences in simulated mean yields of winter wheat were evident for the different resolutions ranging from 1 km × 1 km to 100 km × 100 km across Germany. However, high resolution input data was essential to reproduce spatial variability of heat and drought stress for the more heterogeneous regions.
4. Two regional studies were arranged to evaluate the interactions between management and climate on crop production under climate change conditions. A crop model (DSSAT v4.5) was employed to assess the interactions between fertilization management of pearl millet (Pennisetum americanum L.), crop substitution [pearl millet instead of maize (Zea mays L)], and climate in semi-arid environments of Iran and the Republic of Niger, respectively. The pearl millet biomass production showed a strong response to different fertilization management in Niger. The highest dry matter production of pearl millet was obtained in combination with crop residues and mineral fertilizer treatment. The dry matter production of pearl millet was reduced by 11% to 62% under different climate change scenarios and future time periods (2011-2030 and 2080-2099). Results of this study showed that higher soil fertility could compensate for the negative effects of high temperature on biomass production. This was a result of the strong positive relationship between biomass production and the sum of precipitation under high soil fertility.
Crop substitution as an adaptation strategy (new hybrids of pearl millet instead of maize) enhanced fodder production and water use efficiency in present and potential future climatic conditions in northeast Iran. However, the fodder production of both crops was reduced due to shortening of the period from floral initiation to the end of leaf growth under various climate change conditions. Benefits of crop substitution may decline under climate change resulting in higher temperature sensitivity of the new hybrids of pearl millet.
Several conclusions were drawn from this study: It is necessary to consider canopy temperature instead of air temperature in crop models and use data from experiments under field conditions to improve and properly calibrate crop models for heat and drought stress responses. Crop models must also consider that effects of heat and drought stress on crops differ with phenological phases and can be compensated for by responses of other processes. An increase in the intensity of heat stress around anthesis can, for instance, be fully compensated for by the advancement in phenology in winter cereals under climate change. It is not necessary to use high resolution weather and soil input data for simulating the effects of heat and drought stress on crop yield at a national scale; but, high resolution input data are necessary to reproduce spatial patterns of heat and drought. Finally, implementation of management practices in cropping systems may change the response of crops to climate change. For this reason, management practices should be considered as an adaptation strategy.
... Production ecology sees plant production as linked to or embedded in a wide variety of social systems, like households and village communities but also farmer cooperatives and political-administrative regimes. Research efforts are geared towards the identification of physical, physiological, ecological processes that can contribute or hamper efficient use of natural resources ( Van de Ven et al., 2003), also paying attention to socio-economic constraints of plant production (Van Ittersum and Rabbinge, 1997) at farm or sector level. More specifically, production ecologists collect data and generate insights that can help to reduce differences between potential and actual yield levels: the so-called 'yield gap' (Van Ittersum and Rabbinge, 1997). ...
The production and expansion of palm oil have emerged as a major and controversial issue in political and public debates in the North and the South on sustainable food and agriculture. Scientific research has played a marginal role in these debates that are characterized by black and white views on palm oil as a good, bad or even ugly crop, and by solutions that are limited in scope. Our first argument is that new conceptualization of the complexity and dynamics of the palm oil sector can revitalize debate on sustainable palm oil and be used to identify sustainable pathways for palm oil production. For this purpose, we develop an interdisciplinary framework, conceptualizing the palm oil sector as consisting of systems, flows and networks. Our second argument is that a transdisciplinary approach is need to identify and develop sustainable pathways. We present six ideas on how to do so. Given the controversy in debates on the production and expansion of palm oil, we consider switchers as critical actors for shaping sustainable pathways, both in the palm oil sector and at the science-policy interface.
... In many agricultural production systems an animal component is included, in addition to the plant component (Van de Ven et al., 2003). Meanwhile the educational background of most feedlot farmer is elementary school. ...
This study was aimed : (i) to know the subsystem implementation and agribusiness planning in beef cattle fattening; (ii) to count the profit of beef cattle farming; (iii) to analyze the effect of agribusiness subsystem implementation and agribusiness planning to beef cattle fattening profit. This study was carried out using survey method and the elementary units were feedlot farmers. The sample was determined by Purposive Quota Sampling Method on 112 respondents spread across five regencies, namely Blora, Rembang, Grobogan, Wonogiri, and Boyolali. Data were collected from primary and secondary sources. The data analysis used quantitative descriptive and inferential statistics method, which include scoring, financial, and multiple linear regression analysis. The results showed that : (i) the implementation of agribusiness subsystem (including preproduction subsystem, marketing, and agribusiness support services) and agribusiness planning were not so good category, while the cattle farming subsystem was moderate category; (ii) the average of farming scale in each feedlot farmer was 2.95 head of cattle with the profit rate was IDR 1,044,719 per fattening period during 6.68 months (equivalent to IDR 156,395 per month); (iii) agribusiness subsystem and agribusiness planning had significant impact on feedlot farmer profit simultaneously, but preproduction subsystem and the agribusiness support services subsystem partially had a significant impact on feedlot farmer profit.
... Here, yield-defining factors are animal species, breed and sex, and temperature, while yieldlimiting factors are the availability and quality of feed and water. Main yield-reducing factors are diseases, animal wellbeing concerns and pollutants (van de Ven et al. 2003). There is much information about differences between regions and farms in actual yields of dairy and beef production systems, but there is little information about the gap between potential and actual yields in practice. ...
The increasing demand for safe and nutritional dairy and beef products in a globalising world, together with the needs to increase resource use efficiency and to protect biodiversity, provide strong incentives for intensification of grassland and forage use. This paper addresses the question: 'Does intensification of grassland and forage use lead to efficient, profitable and sustainable ecosystems?' We present some notions about intensification of agricultural production, and then discuss the intensification of grassland-based dairy production in The Netherlands, Chile and New Zealand. Finally, we arrive at some conclusions. External driving forces and the need to economise (the law of the optimum) provide strong incentives for intensification, that is, for increasing the output per unit surface area and labour. The three country cases illustrate that intensification of grassland use is a global phenomenon, with winners and losers. Winners are farmers who are able to achieve a high return on investments. Losers are small farmers who drop out of the business unless they broaden their income base. The relationship between intensification and environmental impact is complex. Within certain ranges, intensification leads to increased emissions of nutrients and greenhouse gases to air and use of water per unit surface area, but to decreased emissions when expressed per unit of product. The sustainability of a grassland-based ecosystem is ultimately defined by the societal appreciation of that system and by biophysical and socioeconomic constraints. In conclusion, intensification may lead to more efficient and profitable and, thereby, more sustainable grassland ecosystems. This holds especially for those systems that are currently not sustainable because they are either underutilised and of low productivity or over-exploited and unregulated, and as long as the adapted systems meet societal and ecological constraints.
... The model LIVSIM is a dynamic model based on the principles of production ecology (Van de Ven et al., 2003). LIVSIM simulates the performance of individual animals in time according to growth-defining factors (genetic characteristics), and growth-limiting factors (feed quantity and quality). ...
Until the turn of the century, farmers in West Africa considered cotton to be the ‘white gold’ for their livelihoods. Large fluctuations in cotton prices have led farmers to innovate into other business including dairy. Yet the productivity of cows fed traditional diets is very poor, especially during the long dry season. This study combines earlier published results of farmer participatory experiments with simulation modelling to evaluate the lifetime productivity of cows under varying feeding strategies and the resulting economic performance at farm level. We compared the profitability of cotton production to the innovation of dairy. The results show that milk production of the West African Méré breed could be expanded if cows are supplemented and kept stall-fed during the dry season. This option seems to be profitable for better-off farmers, but whether dairy will replace (some of) the role of cotton as the white gold for these smallholder farmers will depend on the cross price elasticity of cotton and milk. Farmers may (partly) replace cotton production for fodder production to produce milk if the price of cotton remains poor (below US$0.35/kg) and the milk price relatively strong (higher than US$0.38/kg). Price ratios need to remain stable over several seasons given the investments required for a change in production strategy. Furthermore, farmers will only seize the opportunity to engage in dairy if marketing infrastructure and milk markets are further developed.
... Following numerous other studies, factors of production controlled by the farmer, X, will include seed varieties and application rates, crop mixtures (i.e. whether maize is intercropped with a nitrogen fixing plant 4 , or whether it is forced to compete with another field crop such as millet), whether the field is weeded throughout the growing season, tillage method and tillage timing (Lichtenberg and Zilberman, 1986;Paris, 1992;Rabbinge, 1993 Chambers andLichtenberg, 1994;Chambers and Lichtenberg, 1996;Carpentier and Weaver, 1997;van Ittersum and Rabbinge, 1997;Saha, Shumway and Havenner, 1997;Berck, Geoghegan and Stohs, 2000;Oude Lansink and Carpentier, 2001;Holloway and Paris, 2002;van de Ven et al, 2003;Guan et al, 2006) 5 . With respect to timing, we control for whether planting took place prior to the first rains, when there is annual "nitrogen flush" into the soil from organic material that has decomposed throughout the dry season (Haggblade and Plerhoples, 2010). ...
Chapter 1 is an analysis of the determinants of maize yield response to fertilizer applications using longitudinal data collected in 2004 and 2008 from 7,127 smallholder maize fields. The Instrumented Pooled Correlated Random Effects estimator is employed to control for simultaneity and omitted variables. The model is specified such that response rates to fertilizer application are conditional on certain farmer practices and the agro-ecological conditions under which maize is grown.
Findings indicate top dressing is more effective than basal fertilizer on Zambian soils with average response rates of 4.3 kg/kg and 3.0 kg/kg respectively. This however masks a wide range of variability in fertilizer’s effectiveness. Top dressing response rates, for example, can be nearly 50% lower on coarse, sandy soils and on plowed fields where the majority of the topsoil is disturbed. Basal fertilizer is vulnerable to nutrient “lockup” in the acidic soils that prevail throughout Zambia. Average marginal yield response to basal fertilizer is just 2.1 kg/kg on the highly acidic soils where 51% of our sample fields are located. On semi-neutral soils, response rates can more than triple up to 7.6 kg/kg on average. Unfortunately, only 2% of our sample (and a similar proportion of all Zambian maize fields) are in areas where semi-neutral soils prevail.
Given transportation costs and the estimated average product of fertilizers, this study suggests fertilizer use is not profitable for most Zambian farmers at commercial prices. This finding has important implications for the long-run viability of subsidy programs. Specifically, if fertilizer is unprofitable for farmers commercially, there is no possibility for a successful “phase out” of a subsidy program after which farmers would continue to use commercial fertilizer.
Chapter 2 addresses issues pertaining to marketing and trade policies. Expensive interventionist grain marketing and trade policies in many Southern African countries are frequently born from uncertainty regarding potential private sector performance. These policies have limited the activity of the private sector, which perpetuates the uncertainty over its potential performance. Indeed, many studies conclude that grain markets in Southern Africa are not integrated with each other and other world markets at least partially due to government policies and the transfer costs they impose.
This study employs the price transmission model introduced by Myers and Jayne (2012) using data from various sources to determine whether long-run spatial price equilibriums exist, and to measure the speed at which price shocks are transmitted. The innovation in this research is the focus on markets that are connected through informal trade across international borders, specifically focusing on a pair of markets in Zambia and The Democratic Republic of Congo and a pair of markets in Malawi and Mozambique.
The study shows an example of the price relationship between markets that are relatively unimpeded by interventionist trade policies. We find that when we control for transfer costs, markets in the Southern Africa region can be expected to perform in accordance with economic theory; a long-run price equilibrium will exist, arbitrage seems to be be carried out competitively, and price transmission is appears to be fairly rapid.
... The role of catchments (or watersheds) in providing ecological services to agriculture has often been underrecognised, although there is growing interest among mainstream researchers in evaluating methods of integrating farmland, natural vegetation, water bodies and other landscape features (Stirzaker et al. 2000, van de Ven et al. 2003. In addition to work on the ecological services provided by diverse landscapes (see Chapter 4), research on systems design has investigated perennial systems, farm layout, crop rotations, water management and so on (Doing 1997, Kuiper 1997, Vereijken et al. 1997, see also Chapter 1). ...
It is a historically opportune time to review organic agriculture. Alongside the burgeoning production and trade in organic produce, increased interaction between researchers and organic producers has led to comparable growth in the production of organic knowledge. For example, new research centres have been established in many countries and there is increasing support from private and government funding agencies for organic-specific research. Growing numbers of peer-reviewed journals have been publishing organic agriculture-focused research, many organic conferences have been held around the world and the International Society for Organic Agriculture Research (ISOFAR) is now supporting organic researchers and promoting improved methodologies for researching organic systems.
These activities have created considerable knowledge on organic agriculture, and the opportunity for evaluation. There is now sufficient, robust information to begin reviewing the strengths and weaknesses, assessing the extent to which its claims are validated and identifying ways to improve the sustainability and productivity of organic agriculture. Certainly, some of the work needs further verification or repetition for example, over longer time spans, but the quality of research on organic farming systems has been recognised by the top scientific forums such as the journals Nature and Science.
Despite the impressive growth in the recent past, the outlook for organic farming is not all rosy. The movement has reached a point where there are signs that the large increases in demand may be slowing, contamination by genetically modified crops poses a threat, and criticisms have been raised against certain practices such reliance on tillage and the growing industrialisation of the movement. The organic movement has a history of almost 100 years, with over 50 years of continuous production on some farms. In addition, there has been huge growth of the organisations that underpin the organic movement and study by mainstream researchers over the last 30 years. Now it is time to evaluate, reflect and revise.
The objectives of this book are to:
• describe and critically review key aspects of organic agriculture such as soil fertility management, plant and animal production, social and environmental issues, as well as training and research;
• maintain a global perspective by drawing on a multinational team of authors and referring to the widest available data in each section;
• combine in one volume the insights of international experts who have direct experience with the organic movement, from on-farm work and teaching to marketing and rural appraisal; and
• provide a unique and timely science-based resource for researchers, teachers, extensionists, students, primary producers and others around the world.
There are five main sections to the book. The first section provides a general introduction to the organic movement followed by reviews of key agricultural production issues such as managing soils, plants and livestock, and breeding plants and animals for organic farming systems. The second section deals with overarching regulatory and management concerns including developing effective and verifiable organic standards and certification processes, as well as economic and marketing considerations. Section three contains chapters addressing the external or off-farm issues, topics that are especially relevant ‘beyond the farm gate’. The environmental and social impacts of organic farming are reviewed and differences in food quality between farming systems are also discussed in detail. The fourth section deals with topics related to developing a knowledge base and building human capacity for organic agriculture. The key themes in this section are research, education, extension and training. The final section provides a summary of the key issues and challenges raised in the book.
The book gathers together a range of specialists with direct experience with organic farming over many years. Authors from over a dozen countries in several continents have contributed their knowledge to the book, making it more than just another Eurocentric or North American perspective on organic agriculture. A special feature of the book is a series of five ‘Special topics’, smaller sections that address key questions or challenges facing organic agriculture. These sections are intended to provide a more detailed analysis of specific issues that cannot be covered as sufficiently in the larger general chapters.
The book is not designed to be a set of production and marketing guidelines or a ‘how to do it’ manual for organic producers; nor are the reviews intended to be uncritical descriptions of organic principles and practices. Instead the reviews provide an objective and rigorous critique of the issues covered. The purpose is to evaluate, rather than advocate, organic agriculture. Some of the reviews presented may be limited by the lack of available data, especially outside western Europe and North America. The small size of the organic movement and lack of government and business support over several decades have meant that important data sets (e.g. economics of conversion) have not been collected in many regions or at all.
This book is intended to be a unique and indispensable resource that offers a diverse range of valuable information, data and perspectives on organic agriculture at a time when the world community is increasingly aware of the problems of our current agricultural practices and the importance of creating sustainable agricultural and systems for the long-term health of humankind and the biosphere as a whole.
... Yield gap between actual yield and attainable yield is caused by the growth reducing factors such as weeds, pests, and diseases. Potential yield is typically not achieved due to growth limiting and growth reducing factors; also, it may not be economically viable to attempt to achieve potential yield(Rabbinge 1993; Van Ittersum and Rabbinge 1997;Van de Ven et al. 2003). ...
... Animal production is a function of both amount and quality of feed intake (Kamalzadeh et al., 1997; Ryan et al., 1993). Availability of high quality feed will lead to intake in accordance with the animal's energy requirements, so that potential liveweight and/or milk production can be achieved (Van de Ven et al., 2003). In situations where both low and high quality feeds are available, farmers have to select the best quality feeds, or a limited number of animals should be allowed to select, to achieve optimum production for a given production objective (Zemmelink, 1995). ...
To evaluate the sustainability of agricultural systems, the dynamics of nitrogen (N), phosphorus (P) and potassium (K) were
studied at field and farm scales in Teghane micro-catchment, Northern Highlands of Ethiopia. Three farm wealth groups (rich,
medium and poor) were distinguished based on farm size, capital assets and grain stocks. The NUTMON questionnaire and software
have been used for data collection and calculation of partial macronutrient balances. The study indicates that total input
to farm fields of all three macronutrients does not balance nutrient removal in crop yield and animal feeds. Consequently,
N, P and K stocks in the soil are rapidly declining, with annual depletion rates higher for the rich group (2.4% of total
N, 1.3% of total P and 1.3% of total K) than for the poor group (1.0% of total N, 0.2% of total P and 0.4% of total K), and
the medium group taking an intermediate position. For all three groups, current farm management is not sustainable. The study
clearly identifies the need for the development of integrated nutrient management systems to reduce the high rates of nutrient
depletion and to transfer to sustainable farm management systems. Three possible measures can be suggested: First, improvements
in nutrient use efficiency from manure, which could be attained through judicious management, i.e. manure must be carefully
stored to minimize physical loss of the manure/compost and nutrients, and that manure must be applied to the appropriate crop
with the appropriate method at the proper time. Secondly, introduction of energy-saving stoves to reduce use of cattle dung
for fuel and consequently increasing manure availability for field application. Thirdly, application of more external chemical
fertilizer, together with improved rainwater harvesting for supplementary moisture supply.
... Animal production is a function of both amount and quality of feed intake ( Kamalzadeh et al., 1997;Ryan et al., 1993). Availability of high quality feed will lead to intake in accordance with the animal's energy requirements, so that potential live weight gain and/or milk production can be achieved (Van de Ven et al., 2003). In situations where both low and high quality feeds are available, farmers have to select the best quality feeds or a limited number of animals should be allowed to select the best feeds, to achieve optimum production for a given production objective (Zemmelink, 1995). ...
In the Northern Highlands of Ethiopia, integrated crop-livestock production within smallholder farms is the dominant form of agricultural production. Feed availability and quality are serious constraints to livestock production in Ethiopia in general, and in its Northern Highlands in particular. The objective of this study was to describe the relationship between feed availability and quality and live weight gain, milk and manure production and the soil C balance in Teghane, Northern Highlands of Ethiopia. The so-called JAVA model procedure, that essentially predicts metabolizable energy intake and animal production on the basis of feed quality and quantity, has been used and linked to a soil carbon balance. Forages were ranked according to their quality (on the basis of metabolizable energy intake by livestock) in descending order. Rations were formulated by stepwise including components of increasingly lower quality to calculate the trade-offs between feed quantity and quality. In the model, the soil C balance was described in relation to soil organic matter decomposition, C input from roots, grazing and/or harvesting losses, feed residues and manure. Moreover, an analysis of monetary values of live weight gain/loss, manure and draught power is included. The results of the model showed that mean daily live weight gain and milk production per TLU continuously increased with decreasing herd size, while total annual live weight gain reached a maximum (62 Mg) at the use of the 30% best feeds and a herd size of 630 TLU. Soil C balance at this level of feed use is negative and deteriorates with increasing feed use. The model estimated an optimum herd size of 926 TLU to attain the maximum combined monetary value of live weight gain, manure and draught power at 50% feed use. Actual herd size in the study area was 1506 TLU. Our results indicate that in areas where feeds of very different quality are available, maximum benefits from meat and/or milk production and soil C balance can be attained by selective utilization of the best quality feeds, through a storage and carry-over system.
... The basic approach used in the NUANCES-FARMSIM model follows the Wageningen school of agro-ecological modelling in its use of the hierarchy in growth and production factors and of the determination of efficiencies to define production levels ( Van de Ven et al., 2003;. The limiting and reducing factors are the focus of interactions between socio-economic factors such as labour availability and allocation and their effects on crop and livestock productivity (see below). ...
African smallholder farming systems are complex, dynamic systems with many interacting biophysical subcomponents. In these systems the major inputs and outputs are managed by human agency – the farmers. To analyse potential developmental pathways of smallholder farming systems in sub-Saharan Africa (SSA), we recognised the need for a tool that can capture the effects and consequences of decision-making on the use of resources. Here we describe and apply such a new modelling tool, developed within the NUANCES framework (Nutrient Use in ANimal and Cropping systems: Efficiencies and Scales), called NUANCES-FARMSIM (FARM SIMulator), an integrated crop – livestock model developed to analyse African smallholder farm systems. NUANCES-FARMSIM was used to analyse a representative case study farm in the highlands of Western Kenya, a site for which each of the components of FARMSIM has been thoroughly tested. We present the results of a sensitivity analysis which showed the model to be sufficiently robust to identify key management options that explain most of the variability in farm productivity, and the long-term consequences of these options for the case study farm. The analyses showed clearly that the most important decisions are those related to the interactions between the different components of the farm and therefore justify the need of integrating crop and livestock components within one modelling tool. The allocation of limited resources across the farm, and the way organic matter is recycled or redistributed within the farm determines the long-term production capacity of the system. The results of the sensitivity analyses further showed that for the case study farm in Western Kenya a strong focus on improving the reliability of the subsystem level or process descriptions will only result in minor improvement in simulating productivity at farm level.
... The main constraints to the production of dairy systems identified for Central Kenya are seasonal fluctuations of feed availability, poor feed quality and labour availability (Staal et al., 2001), and high mortality rates in all age classes (Bebe et al., 2003b). Model description LIVSIM (LIVestock SIMulator) is a dynamic model based on the principles of production ecology (Van de Ven et al., 2003). Following these principles, LIVSIM simulates the performance of individual animals in time according to their genetic potential and feeding. ...
Evaluation of lifetime productivity is sensible to target interventions for improving productivity of smallholder dairy systems in the highlands of East Africa, because cows are normally not disposed of based on productive reasons. Feeding strategies and involuntary culling may have long-term effects on productive (and therefore economic) performance of dairy systems. Because of the temporal scale needed to evaluate lifetime productivity, experimentation with feedstuffs in single lactations is not enough to assess improvements in productivity. A dynamic modelling approach was used to explore the effect of feeding strategies on the lifetime productivity of dairy cattle. We used LIVSIM (LIVestock SIMulator), an individual-based, dynamic model in which performance depends on genetic potential of the breed and feeding. We tested the model for the highlands of Central Kenya, and simulated individual animals throughout their lifetime using scenarios with different diets based on common feedstuffs used in these systems (Napier grass, maize stover and dairy concentrates), with and without imposing random mortality on different age classes. The simulations showed that it is possible to maximise lifetime productivity by supplementing concentrates to meet the nutrient requirements of cattle during lactation, and during early development to reduce age at first calving and extend productive life. Avoiding undernutrition during the dry period by supplementing the diet with 0.5 kg of concentrates per day helped to increase productivity and productive life, but in practice farmers may not perceive the immediate economic benefits because the results of this practice are manifested through a cumulative, long-term effect. Survival analyses indicated that unsupplemented diets prolong calving intervals and therefore, reduce lifetime productivity. The simulations with imposed random mortality showed a reduction of 43% to 65% in all productivity indicators. Milk production may be increased on average by 1400 kg per lactation by supplementing the diet with 5 kg of concentrates during early lactation and 1 kg during late lactation, although the optimal supplementation may change according to milk and concentrate prices. Reducing involuntary culling must be included as a key goal when designing interventions to improve productivity and sustainability of smallholder dairy systems, because increasing lifetime productivity may have a larger impact on smallholders' income than interventions targeted to only improving daily milk yields through feeding strategies.
... Intensive pig production generates large amounts of manure that are applied to a limited land area. The fertiliser application rate in intensive agricultural systems has increased dramatically in recent years in subtropical areas of China, India and Australia, 17 due to the relatively high economic value of extra yields and the difficulty of managing the manure supply in these systems. Continuous application of manure may lead to series environmental problems, such as air polluted with odour, water pollution and the accumulation of heavy metals in soil. ...
Pig production plays an important role in farming systems worldwide, especially in subtropical areas. The past few decades have seen significant changes in swine production in such regions. However, there are regional differences in pig production, and some of these are associated with serious problems which impact production systems, the environment and human health. This review introduces the pig breeds, crops and challenge of pig production that faces subtropical areas. A detailed analysis focuses on the control of production problems that are affected by limitations in management and nutritional strategies. Then, factors that drive the major changes in the pig industry in this area are examined in detail, and some insight into pig production directions is provided.
... Yield gap between actual yield and attainable yield is caused by the growth reducing factors such as weeds, pests, and diseases. Potential yield is typically not achieved due to growth limiting and growth reducing factors; also, it may not be economically viable to attempt to achieve potential yield (Rabbinge, 1993;Van Ittersum and Rabbinge, 1997;Van de Ven et al., 2003). involved in the basic biological process, but can help create or alter growth conditions under which growth inputs take effect. ...
Fertilizer use remains very low in most of Africa despite widespread agreement that much higher use rates are required for sustained agricultural productivity growth. This study estimates maize yield response functions in agro-ecological Zone IIA, a relatively high potential zone of Zambia, to determine the profitability of fertilizer use under a range of small farm conditions found within this zone. The theoretical framework used in this study incorporates agronomic principles of the crop growth process. The model distinguishes different roles of inputs and non-input factors in crop production. We estimate the effects of conventional production inputs as well as household characteristics and government programs on maize yield for households in the dominant acrisols soil type. Results indicate that even within this particular soil type within Zone IIA, the maize-fertilizer response rate in the two specific years varied widely across households. The main factors explaining the variability in maize-fertilizer response rates were the rate of application, the timeliness of fertilizer availability, the use of animal draught power during land preparation, and whether the household incurred the death of an adult member in the past three years. These modifying factors, as well as variations in input and output prices due to proximity to roads and markets, substantially affected the profitability of fertilizer use on maize. Fertilizer use on maize tended to be unprofitable at full commercial fertilizer prices for farmers who received fertilizer late and who were located in relatively remote areas.
... Alternative dairy systems are based on a target-oriented approach (Van de Ven et al., 2003; Aarts et al., 1999): users set a predefined milk yield for which the livestock component calculates the nutrient requirements. This differs from current dairy systems in that the livestock component determines the replacement rate based on the relationship between milk yield and replacement rate derived from the SS (Figure 3.1). ...
This document summarises the development of a ruminant livestock component for the Farm System Simulator (FSSIM). This includes treatments of energy and protein transactions in ruminant livestock that have been used as a basis for the biophysical simulations that will generate the input production parameters for FSSIM. The treatments are derived principally from the “French” feed evaluation and rationing system for protein and energy. Currently, we have constructed routines that are capable of simulating input-output relationships for energy and protein in the following representative systems; dairy cattle; suckler cows; growing and finishing cattle; sheep and goats. The calculations of energy and protein requirements for these classes of livestock are described in detail in this document
Integrated systems allow the redesign of productive landscapes due to the insertion of different species of trees and shrubs. A diversified pasture provides the animal with a wider range and a greater amount of phytonutrients than animals fed on grains, and beyond that, tree legumes have great potential for producing biomass with excellent levels of crude protein, as well as the capacity for symbiotic nitrogen fixation. Assuming that modeling can be a relevant tool to address systemic changes, we sought to answer the following question: “how can ruminant husbandry systems be modeled to help farmers, considering the combination of pasture and crop production?” Thus, this work aims to create a modeling framework to guide the redesign of productive landscapes for ruminants in tropical conditions at the farm level. The activities to be carried out will be divided into four stages: a) bibliographical research on existing indicators and/or models for ruminant livestock farming; b) writing opinion articles (already published) and review articles (this article); c) indicating parameters for modeling the redesign of ruminant production landscapes with the use of multifunctional forage plants; and d) demonstrating the novelty by building a decision-making model for rural properties. The hypothesis of this work is that the redesign of multifunctional production landscapes can be guided by modeling obtained from experimental variables that already exist and/or are under construction, as well as from published literature.
This article assesses the effect of climate change on livestock production in Sub‐Saharan Africa, for a sample of 45 countries over the period 2000–2021. Using a two‐factor fixed effects panel data model, our results obtained by the two‐way fixed effects estimator show that (i) climate change negatively influences livestock production through high temperatures, while abundant rainfall is beneficial. (ii) Through transmission channels, we find that maize price volatility exacerbates the negative effect of rising temperatures on livestock production, while it reduces the beneficial effect of abundant rainfall. Furthermore, we find that water availability mitigates the adverse effect of rising temperatures on livestock, while enhancing the beneficial effect of rainfall on livestock. Finally, we concede that conflicts reduce the beneficial effect of rainfall on livestock production. To increase livestock production in Sub‐Saharan Africa, we recommend: the practice of pastoralism, based on the production of plants and fodder adapted to climate change, the improvement of animal nutrition, and the inclusion of breeders in the decision‐making process in the cattle industry.
Smallholder farming systems in southern Africa are characterized by low-input management and integrated livestock and crop production. Low yields and dry-season feed shortages are common. To meet growing food demands, sustainable intensification (SI) of these systems is an important policy goal. While mixed crop–livestock farming may offer greater productivity, it implies trade-offs between feed supply, soil nutrient replenishment, soil carbon accumulation, and other ecosystem functions (ESFs) and ecosystem services (ESSs). Such settings require a detailed system understanding to assess the performance of prevalent management practices and identify potential SI strategies. Models can evaluate different management scenarios on extensive spatiotemporal scales and help identify suitable management strategies. Here, we linked the process-based models APSIM (Agricultural Production Systems sIMulator) for cropland and aDGVM2 (Adaptive Dynamic Global Vegetation Model) for rangeland to investigate the effects of (i) current management practices (minimum input crop–livestock agriculture), (ii) an SI scenario for crop production (with dry-season cropland grazing), and (iii) a scenario with separated rangeland and cropland management (livestock exclusion from cropland) in two representative villages of the Limpopo Province, South Africa, for the period from 2000 to 2010. We focused on the following ESFs and ESSs provided by cropland and rangeland: yield and feed provision, soil carbon storage, cropland leaf area index (LAI), and soil water. Village surveys informed the models of farming practices, livelihood conditions, and environmental circumstances. We found that modest SI measures (small fertilizer quantities, weeding, and crop rotation) led to moderate yield increases of between a factor of 1.2 and 1.6 and reduced soil carbon loss, but they sometimes caused increased growing-season water limitation effects. Thus, SI effects strongly varied between years. Dry-season crop residue grazing reduced feed deficits by approximately a factor of 2 compared with the rangeland-only scenario, but it could not fully compensate for the deficits during the dry-to-wet season transition. We expect that targeted deficit irrigation or measures to improve water retention and the soil water holding capacity may enhance SI efforts. Off-field residue feeding during the dry-to-wet season transition could further reduce feed deficits and decrease rangeland grazing pressure during the early growing season. We argue that integrative modeling frameworks are needed to evaluate landscape-level interactions between ecosystem components, evaluate the climate resilience of landscape-level ecosystem services, and identify effective mitigation and adaptation strategies.
Narrowing the yield gap is a crucial way to increase grain production without increasing cultivated land to meet the growing demand for food and nutrients. Although estimation of yield gap in major grains (rice, wheat and maize) has been extensively studied at the field and experimental levels in recent decades, hardly any rigorous evidence-based studies have been performed at the farm level for the last decade in China. In this study, we employ production functions to estimate the yield gaps in rice farming and examine the different rice gaps in, e.g., regions and cultivation systems, using data collected from 1,029 farms in China in 2017. The results show that rice yield gap still existed in the surveyed region, although almost all farms had already achieved their potential yield (with a 2% yield gap). Age and distance between homesteads and plot have a negative effect on the yield gap, while household assets play an important role in narrowing the yield gap at the household level. Yield gap also varies considerably among regions, cultivated systems (i.e., early, one-season and late rice) and varieties. Policies aimed at ensuring grain self-sufficiency are suggested to focus more on technological changes to improve the potential yield rather relying on narrowing the yield gap at the household/farm level in rice production.
The difference between the theoretical maximum (potential) production and the actual production realized by farmers is referred to as the yield gap. The objectives of this study are to develop a mechanistic model for dairy cows that allows yield gap analysis in dairy production systems and to evaluate model performance. We extended and adapted an existing model for beef cattle to dairy cattle, and the new model was named Livestock simulator for Generic analysis of Animal Production Systems-Dairy cattle (LiGAPS-Dairy). Milk production and growth of an individual cow over its entire lifespan were described as a function of the animal's genotype, the ambient climate, feed quality, and available feed quantity. The model was parameterized for Holstein-Friesian cows. After calibration, we evaluated model performance by comparing simulated results and measured results from experimental farms in the Netherlands, which were not used for model calibration. Cows were permanently housed in stables, where the diet consisted of predetermined amounts of concentrates and ad libitum high-quality roughage. The mean absolute error (MAE) for simulated milk production per lactation was 12% of the measured milk production, whereas the MAE for simulated daily milk yields was 19%. The MAE for simulated feed intake per lactation was 10% of the measured feed intake, whereas the MAE for simulated daily feed intake was 19%. The average yield gap for dairy cows was 11% of the potential milk production (YP). Yield gap analysis indicated that for experimental farms in the Netherlands, the difference between YP and feed quality limited milk production (YL) of 1,009 kg fat- and protein-corrected milk was mainly explained by feed intake capacity (33%), protein deficiency (25%), cow weight at the start of experiments (23%), and heat stress (19%). The LiGAPS-Dairy model also indicated the periods during lactation in which these factors affected milk production. In our opinion, the overall model performance is acceptable for permanently housed cows under Dutch conditions. The model needs to be evaluated further for other production systems, countries and breeds. Thereafter, LiGAPS-Dairy can be used for yield gap analysis and exploration of options to increase resource use efficiency in dairy production.
Quantifying how multiple ecosystem services and functions are affected by different drivers of Global Change is challenging. Particularly in African savanna regions, highly integrated land-use activities created a landscape mosaic with flows of multiple resources between land use types. A framework is needed that quantifies the effects of climate change, management and policy interventions on ecosystem services that are most relevant for rural communities, such as provision of food, feed, carbon sequestration, nutrient cycling and natural pest control. In spite of progress made in ecosystem modelling, data availability and stakeholder interactions, these elements have neither been brought together in an integrated framework, nor evaluated in the context of real-world problems. Here, we propose and outline such framework as developed by a multidisciplinary research network, the Southern African Limpopo Landscapes network (SALLnet). Components of the framework such as the crop model APSIM and the vegetation model aDGVM2 had already been parameterized and evaluated using data sets from savanna regions of eastern, western and southern Africa, and were fine-tuned using novel data sets from Limpopo. A prototype of an agent-based farm household model was developed using comprehensive farm survey information from the Limpopo Province of South Africa. A first test of the functionality of the integrated framework has been performed for alternative policy interventions on smallholder crop-livestock systems. We discuss the versatile applicability of the framework, with a focus on smallholder landscapes in the savanna regions of southern Africa that are considered hotspots of global change impacts.
طراحی یک سیستم تولید به فرایند تغییرات عمدی اطلاق می شود که براساس دانش موجود ابزارهای ممکن مدل سازی دانشهای جدید تولید شده و پیشنهادهای ابتکاری می باشد بطور کلی طراحی سیستم تولید تلفیقی براساس مجموعه ای ازراه کارهایی صورت میگیرد که تاکید ویژه ای برمحیط تولید و شرایط اکولوژیکی اقتصادی و اجتماعی دارند براساس بسیاری ازتحقیقات بهره گیری ازاینگونه سیستم ها می تواند سبب امنیت غذایی حفظ محیط زیست اصلاح دربهره وری ازمنابع درنهایت سوداقتصادی بیشتر گردد و ازسوی دیگر هماهنگی بیشتری را با نیازهای اجتماعی کشاورز دارا می باشد ولی با وجود کلیه فواید ذکر شده درنظر گرفتن این نکته ضروری است که بعدازطراحی هرسیستمتولیدی پذیرش آن توسط کشاورزان زمانبر خواهد بود و این زمان بسته به شرایط اجتماعی و کارایی سیستم متفاوت می باشد.
We present a method for modeling livestock production systems (MLPS), based on unified modeling language. A livestock production system is viewed as having four aspects: production, decision, action and resource. A three-stage process is used. First, the main interactions between the production system and external systems are modeled for each aspect by means of use case diagrams. Second, internal processes detail these use cases with refined use case, sequence or activity diagrams. Third, the model is built. Package and class diagrams model its structure, while sequence, activity, statechart and time diagrams represent its dynamics. We applied MPLS to a simplified beef production example.
African smallholder farming systems are complex, dynamic systems with many interacting biophysical subcomponents. In these systems the major inputs and outputs are managed by human agency – the farmers. To analyse potential developmental pathways of smallholder farming systems in sub-Saharan Africa (SSA), we recognised the need for a tool that can capture the effects and consequences of decision making on the use of resources. Here we describe and apply such a new modelling tool, developed within the NUANCES framework (Nutrient Use in ANimal and Cropping systems: Efficiencies and Scales), called NUANCES-FARMSIM (FARM SIMulator), an integrated crop – livestock model developed to analyse African smallholder farm systems. NUANCES-FARMSIM was used to analyse a representative case study farm in the highlands of Western Kenya, a site for which each of the components of FARMSIM has been thoroughly tested. We present the results of a sensitivity analysis which showed the model to be sufficiently robust to identify key management options that explain most of the variability in farm productivity , and the long-term consequences of these options for the case study farm. The analyses showed clearly that the most important decisions are those related to the interactions between the different components of the farm and therefore justify the need of integrating crop and livestock components within one modelling tool. The allocation of limited resources across the farm, and the way organic matter is recycled or redistributed within the farm determines the long-term production capacity of the system. The results of the sensitivity analyses further showed that for the case study farm in Western Kenya a strong focus on improving the reliability of the subsystem level or process descriptions will only result in minor improvement in simulating productivity at farm level.
Whole-farm design models quantitatively analyze the effects of a variety of potential changes at the farm system level. Science-driven technical information is confronted with value-driven objectives of farmers or other social groupings under explicit assumptions with respect to exogenous variables that are important drivers of agricultural systems (e.g., market conditions). Hence, farm design is an outcome of objective specification and the potential of a system. In recent publications, whole-farm design modelling has been proposed to enhance (farm) innovation processes. A number of operational modelling tools now offers the opportunity to assess the true potential of whole-farm design modelling to enhance innovation. In this paper, we demonstrate that it is not trivial to find niches for the application of goal-based farm models. Model outcomes appeared not to match questions of farm managers monitoring and learning from their own and other farmers’ practices. However, our research indicates that whole-farm design modelling possesses the capabilities to make a valuable contribution to reframing. Reframing is the phenomenon that people feel an urge to discuss and reconsider current objectives and perspectives on a problem. Reframing might take place in a situation (i) of mutually felt dependency between stakeholders, (ii) in which there is sufficient pressure and urgency for stakeholders to explore new problem definitions and make progress. Furthermore, our research suggests that the way the researcher enters a likely niche to introduce a model and/or his or her position in this niche may have significant implications for the potential of models to enhance an innovation process. Therefore, we hypothesize that the chances of capitalizing on modelling expertise are likely to be higher when researchers with such expertise are a logical and more or less permanent component of ongoing trajectories than when these researchers come from outside to purposefully search for a niche.
Fertilizer use remains very low in most of Africa despite widespread agreement that much higher use rates are required for sustained agricultural productivity growth. This study uses longitudinal farm survey data to estimate maize yield response functions in a relatively high-potential zone of Zambia to determine the profitability of fertilizer use under a range of small-farm conditions found within this zone. The theoretical framework used in this study incorporates agronomic principles of the crop growth process. We generalize the asymmetric production models and define a concept of yield scaling factors. The model distinguishes different roles of inputs and non-input factors in crop production. We estimate the effects of conventional production inputs as well as of household characteristics and government programs on maize yield. The results indicate that recommended fertilizer application rates in the two specific years were often unprofitable, given observed price conditions and the yield response to fertilizer. However, there was substantial variability in yield response to fertilizer based upon the rate of application, the timeliness of fertilizer availability, the use of animal draught power during land preparation, and whether the household incurred the death of an adult member in the past three years. These modifying factors, as well as variations in input and output prices due to proximity to roads and markets, substantially affected the profitability of fertilizer use on maize. Copyright (c) 2009 International Association of Agricultural Economists.
Multifunctionality is seen as one of the solutions to society's demand for new functions in the rural areas and the problems with the unsustainability of the agricultural sector in the European Union. In contrast to the traditional functions of income, labor and food production these new functions can not be provided by a single field or a farm. Planning and production of functions like: Nature Conservation, environment and landscape esthetics can only be achieved when the landscape is considered as a whole. We present an outline of a methodology based on concepts and insights from production ecology and landscape ecology, that should enable us to explore the opportunities for multifunctional agriculture, balancing objectives at three spatial scales: field, farm and regional level. The focus of this paper is on the integration of the agricultural production and nature conservation. However, the methodology aims to be easily adaptable for other services. In this paper the concepts of explorative design and habitat networks are explained and integrated to design landscape prototypes. Landscape Prototypes are spatial explicit images of multifunctional agricultural landscapes based on scientific insights and indicating quantitatively the services provided within these virtual landscapes. An important output of the approach are trade-off curves between the different services provided by the landscape. We discuss the implications of our approach for landscape ecological and agronomic research which is on-going in our research program.
In 1991 a new protein evaluation system replaced the Digestible Crude Protein (DCP) system in the Netherlands: the DVE/OEB-system. The system was mainly developed with the aim to prevent avoidable losses of nitrogen, by feeding according to more exactly defined requirements of dairy cows. A second aim was to predict milk protein production more accurately. Protein requirements for maintenance, milk protein production, growth, mobilisation, metabolic losses in the digestive tract and gestation are expressed in DVE, the sum of digestible feed and microbial true protein available in the small intestine. In the system each feed has a DVE-value composed of the digestible true protein contributed by feed protein escaping rumen degradation (1), microbial protein synthesized in the rumen (2) and a correction for endogenous protein losses in the digestive tract (3). Each feed also has a degraded protein balance (OEB) reflecting the difference between the potential microbial protein synthesis based on degraded feed crude protein and that based on energy available for microbial fermentation in the rumen. The framework of the new system is based on what are considered strong elements of other recently developed protein evaluation systems. Additionally new elements are introduced, including undegraded starch (USTA), fermentation products (FP) in ensiled feeds, the role of energy balance in protein supply and the way in which requirements change in the course of lactation. Data within the framework of the system are mainly of Dutch origin. This is particularly true for the regression equations developed to predict the protein values of forages and protein values of a number of by-product ingredients.
The rapidly growing scientific literature on various aspects of carbon storage in soils has given rise to the introduction of several terms when discussing the amounts of carbon that are, or could be, stored in soils. The term “carbon sequestration potential”, in particular, is used with different meanings, sometimes referring to what might be possible given a certain set of management conditions with little regard to soil factors which fundamentally determine carbon storage. An attempt is made to clarify some of the main issues by adopting terminology developed in plant physiology and crop modelling research. This, together with examples from the tropics, is used to clarify some of the issues as relating to mineral soils. The term “Attainablemax” is defined and is suggested as the preferred term for carbon sequestration in mineral soils, being more relevant to management than “potential” and thereby of greater practical value.
The decision support system for agrotechnology transfer (DSSAT) has been in use for the last 15 years by researchers worldwide. This package incorporates models of 16 different crops with software that facilitates the evaluation and application of the crop models for different purposes. Over the last few years, it has become increasingly difficult to maintain the DSSAT crop models, partly due to fact that there were different sets of computer code for different crops with little attention to software design at the level of crop models themselves. Thus, the DSSAT crop models have been re-designed and programmed to facilitate more efficient incorporation of new scientific advances, applications, documentation and maintenance. The basis for the new DSSAT cropping system model (CSM) design is a modular structure in which components separate along scientific discipline lines and are structured to allow easy replacement or addition of modules. It has one Soil module, a Crop Template module which can simulate different crops by defining species input files, an interface to add individual crop models if they have the same design and interface, a Weather module, and a module for dealing with competition for light and water among the soil, plants, and atmosphere. It is also designed for incorporation into various application packages, ranging from those that help researchers adapt and test the CSM to those that operate the DSSAT–CSM to simulate production over time and space for different purposes. In this paper, we describe this new DSSAT–CSM design as well as approaches used to model the primary scientific components (soil, crop, weather, and management). In addition, the paper describes data requirements and methods used for model evaluation. We provide an overview of the hundreds of published studies in which the DSSAT crop models have been used for various applications. The benefits of the new, re-designed DSSAT–CSM will provide considerable opportunities to its developers and others in the scientific community for greater cooperation in interdisciplinary research and in the application of knowledge to solve problems at field, farm, and higher levels.
A Dairy Farming Model was developed to screen the potentials for development of dairy farming on sandy soils in the Netherlands with respect to environmental, agro-technical and economic demands. The Dairy Farming Model consists of technical coefficient generators (TGC models) and an interactive multiple goal linear programming model (IMGLP model). The TCG models have been used to quantify input-output coefficients for a wide range of production techniques for grass, maize, fodder beet and milk. The results of the TCG models have been used in the IMGLP model, that optimizes the set of production techniques with respect to the goals defined.The model has been applied to a fictitious region with sandy soils. The analysis shows that dairy farming can meet both economic and environmental goals, as set by the government for the year 2000. However, this requires a reduction in labour income. Many different dairy farming systems are possible. A few general characteristics are: low N application on grazed grassland, a large proportion of the animals housed in low-emission stables and a substantial part of the concentrates produced in the region itself,Application of the Dairy Farming Model to the situation at the experimental dairy farm 'De Marke' has shown that the model is suited for exploring the opportunities for the development of dairy farming at a specific location, provided it can be initialized for that situation. Initial farm lay-out and measures taken at 'De Marke' have been evaluated.
The methods used for simulating animal performance in the Texas A&M Cattle Production Systems Model are given and discussed. The GRO subroutine is used to calculate feed intake, changes in weight and skeletal size and, if appropriate, milk production level. Growth rates from the GRO subroutine, size, condition, time since calving and the fraction of the animals in the class that were in oestrus the previous month are used in the FERT subroutine to simulate the occurrence of oestrus and conception in open, breeding females. Death rates are simulated in the DIE subroutine as functions of the time of the year, the age and condition of animals in the class and, for cows, whether or not they calved during the current or previous month. The information from these subroutines is used to update the numbers and characteristics of animals in the various classes at the end of each month of simulation.
The production of meat, milk and eggs is highest and occurs at a maximal efficiency if the meteorological elements are within a certain range (zone of indifference). Outside this range the animal has to combat meteorological stress. This requires extra energy, so that less energy is available for productive processes. It is therefore important to find out at which levels the various meteorological elements become stressful to the animal organism. This study has to take into consideration the diversity of domestic animals, both with regard to structural features and functional traits. Responses of various categories of domestic animals to the following potentially stress producing meteorological conditions are briefly reviewed: cold, heat, solar radiation, high altitude and indoor environment. Knowledge so derived can be applied either by adapting the animal to the environment by breeding and selection, or by adapting the environment to the animal by technical and managerial means. Some suggestions are made for future considerations in the field of biometeorology of domestic animals.
Despite the fact that many smallholder farming systems in developing countries revolve around the interactions of crop and livestock enterprises, the modelling of these systems using combinations of detailed crop and livestock models is comparatively under-developed. A wide variety of separate crop and livestock models exists, but the nature of crop–livestock interactions, and their importance in smallholder farming systems, makes their integration difficult. Even where there is adequate understanding of the biophysical processes involved, integrated crop–livestock models may be constrained by lack of reliable data for calibration and validation. The construction from scratch of simulation models that meet the needs of one particular case is generally too costly to countenance. As for all modelling activity, the most efficient way to proceed depends on the nature of the systems under study and the precise questions that have to be addressed. We outline a framework for the integration of detailed biophysical crop and livestock simulation models. We highlight the need for minimum calibration and validation data sets, and conclude by listing various research problems that need attention. The application of robust and trustworthy crop–livestock models is critical for furthering the research agenda associated with animal agriculture in the tropics and subtropics.
This paper describes a formalized approach to identify and engineer future-oriented land use systems. Such land use systems can be used to explore options for strategic decision making with respect to land use policy and to do ex-ante assessment of land use alternatives to be further tested or developed in experimental settings. The so-called goal-oriented approach consists of three steps: (1) goal-oriented identification and design of land use systems; (2) quantification of biophysical production possibilities; and (3) defining the optimal mix of inputs, i.e. the production technique, required to realize production possibilities. The goal-oriented identification and design depends on the land-related objectives of a system under study, whereas plant, animal and environmental characteristics determine biophysical production possibilities. Characteristics of the production technique determine the realization of production possibilities. General guidelines are given to structure the specification and number of alternatives to be explored and to apply agro-ecological principles required for quantification of future-oriented land use systems. Concepts of the approach are illustrated with data from the northern Atlantic zone of Costa Rica and the Sudano–Sahelian zone of Mali. Finally, suggestions are given for the application of the approach at spatial and temporal scales exceeding the field level and time horizon of 1 year.
In this paper, a pedigree of the crop growth simulation models by the ‘School of de Wit’ is presented. The origins and philosophy of this school are traced from de Wit's classical publication on modelling photosynthesis of leaf canopies in 1965. It is shown how changing research goals and priorities over the years have resulted in the evolution of a pedigree of models that are similar in philosophy but differ in level of complexity, the processes addressed and their functionality. In the beginning, modelling was motivated by the quest for scientific insight and the wish to quantify and integrate biophysical processes to explain the observed variation in crop growth. Later, the emphasis of, and funding for, agricultural research shifted towards putting acquired insights to practical and operational use. Model development became led by a demand for tactical and strategic decision support, yield forecasting, land zonation and explorative scenario studies. Modelling developments for different production situations are illustrated using the models the authors consider most important, i.e. BACROS, SUCROS, WOFOST, MACROS and LINTUL, but reference is also made to other models. Finally, comments are made about the usefulness and applicability of these models after nearly 30 years of development, and some future courses of action are suggested.
This paper describes two generic so-called technical coefficient generators, PASTOR (Pasture and Animal System Technical coefficient generatOR) and LUCTOR (Land Use Crop Technical coefficient generatOR), that quantify land use systems in terms of inputs and outputs based on the integration of systems-analytical knowledge, standard agronomic and animal husbandry data and expert knowledge. PASTOR quantifies livestock systems while LUCTOR is geared towards cropping systems. Main inputs quantified include costs, labour requirements, fertiliser use and application of crop protection agents. Outputs are production and a number of associated environmental indicators. Although both PASTOR and LUCTOR were developed to generate input data for land use models, they are also useful as stand-alone tools to explore the technical efficiency of land use systems, to perform cost-benefit analyses and to quantify the trade-off among socio-economic, agronomic and environmental indicators at the field level. PASTOR and LUCTOR are illustrated with data from the Northern Atlantic zone in Costa Rica. Tools such as PASTOR and LUCTOR integrate different types of knowledge, including non-documented knowledge from field experts and make that knowledge transparent and open to critical review and discussion by others.
A new concept of feed intake regulation in ruminants is developed starting from the idea that feed consumption presents both costs and benefits to the animal. For a non-reproducing animal, we consider the intake of net energy for maintenance and gain to be the benefits of feed consumption, and the concomitant consumption of oxygen the costs, since the use of oxygen by tissues indirectly causes an accumulation of damage to cell structures, a loss of vitality, ageing and a limited life span. This leads to the hypothesis that feed intake behaviour will be aimed at maximizing the efficiency of oxygen utilization: from each feed an animal will consume such an amount that the intake of net energy per litre oxygen consumed will be maximal. Testing this hypothesis extensively with data from non-reproducing ruminants shows a good quantitative agreement between predicted and observed ad libitum intake of feeds widely differing in metabolizability, nitrogen content and physical form. Changes in intake parallel to changes of basal metabolism also agree with our hypothesis. Effects on intake of changes in maturity and physiological state are more difficult to test due to insufficient information about the effects of maturity on efficiency of metabolizable energy utilization and uncertainty about the exact nature of costs and benefits of feed consumption in pregnant and lactating animals. Maximization of the efficiency of oxygen utilization may reflect a more universal principle governing the intensity of different forms of behaviour, in ruminants as well as in monogastrics.
Appropriate selection of holistic management strategies for livestock farming systems requires: (1) understanding of the behaviour of, and interrelations between, the different parts of the system; (2) knowledge of the basic objectives of the decision maker managing such enterprise; and (3) understanding of the system as a whole in its agro-ecoregional context. A decision-support system based on simulation and mathematical programming techniques has been built to represent pastoral dairy production systems. The biological aspects (grass growth, grazing, digestion and metabolism, animal performance) are represented by simulation studies under a variety of management regimes. The outputs from the simulation runs (such as pasture utilisation, stocking rates, milk yields, fertilizer use, etc.) are used as data input to the multi-criteria decision-making models, and the latter have been used to select the management strategies which make the most efficient use of the farm's resources (i.e. land, animals, pastures). The paper discusses the effects and implications of different management scenarios and policies on the bio-economic performance of highland dairy farms in Costa Rica. Nevertheless, the model frameworks are generic and can be adapted to different farming systems or ruminant species. The effect of model formulation and sensitivity, different decision-maker objectives, and/or activity or constraint definitions on management strategy selection are also analysed.
Intensive agriculture in The Netherlands has a price in the form of environmental degradation and the diminution of nature and landscape values. A reorientation of farming is needed to find a new balance between economic goals and rural employment, and care for clean water and air, animal well-being, safe food, and the preservation of soil, landscape and biodiversity. The search for farm systems that meet such multiple goals requires a systematic combination of (a) agrotechnical, agroecological and agroeconomic knowledge, with (b) the stakeholders’ joint agreement on normative objectives, to arrive at conceptual new designs followed by (c) empirical work to test, adapt and refine these under real commercial farming conditions. In this paper explorative modelling at the whole farm level is presented as a method that effectively integrates component knowledge at crop or animal level, and outlines the consequences of particular choices on scientific grounds. This enables quantitative consideration of a broad spectrum of alternative farming systems, including very innovative and risky ones, before empirical work starts. It thus contributes to a transparent learning and development process needed to arrive at farm concepts acceptable to both entrepreneurs and society. Three case studies are presented to illustrate the method: dairy farming on sandy soils; highly intensified flower bulb industry in sensitive areas in the western Netherlands; and integrated arable farming. Trade-offs between economic and environmental objectives were assessed in all three cases, as well as virtual farm configurations that best satisfy specified priority settings of objectives. In two of the three cases the mutual reinforcement and true integration of modelling and on-farm empirical research appeared difficult, but for obvious reasons. Only in the flower bulb case was the explorative approach utilized to its full potential by involving a broad platform of stakeholders. The other two case studies lacked such formalised platforms and their impact remained limited. Three critical success factors for explorative modelling are identified: to cover a well-differentiated spectrum of possible production technologies; early timing of modelling work relative to empirical farm prototyping; and involvement of stakeholders throughout.
It would have been very easy to expand on all the sections of the first edition but I decided to try to retain the relatively short, introductory nature of the book. Some new material has been added, particularly where it has been possible to update data, and there has been some change of emphasis in places, in order to reflect changing world conditions. The book retains its original purpose, however, of introducing systems thinking as applied to agriculture. I am grateful to Angela Hoxey for help in preparing this edition, especially in relation to the preparation of tables and figures. C. R. W. SPEDDING v Preface to the First Edition The agricultural systems of the world represent a very large subject. Their study involves a great deal of fairly detailed knowledge, as well as a grasp of the structures and functions of the systems themselves. This book has been written as an introduction to such a study and it concentrates on an overall view, rather than on the detail, partly because of the need to relate the latter to some larger picture in order to appreciate the relevance and significance of the detail. This problem-of seeing the relevance of component studies and the significance of physical, biological and economic detail, and indeed principles-is encountered by many agricultural students right at the beginning of their university careers.
The Department of Theoretical Production Ecology (TPE) at Wageningen Agricultural University (WAU) educates undergraduates and graduates, as well as scientists in post academic courses in the Netherlands and abroad, in systems approaches and simulation and their application in a wide field of agricultural and environmental problems. The supplied courses and programs vary from intensive two-week trainings to series of courses that are followed by supervised research. TPE experienced that the following elements should be present in a successful course or curriculum. Firstly, aims and interests of teachers and participants are in accordance and the level of courses is tuned to the starting level of the participants. Secondly, in a course, systems approaches and simulation can best be applied to a research field relevant to the participants. Colleagues or supervisors advise the participants during the first systems approaches. Thirdly, a whole range of courses, at various levels, can be offered to enhance continuity and to fine tune suppliers to a wide range of demands.
Part I of this thesis contains a critical appraisal of the commonly accepted theory with regard to feed intake regulation in ruminants and the presentation of a new theory. This new theory assumes that feed consumption creates both benefits to the animal (in a non-reproducing animal the intake of net energy for maintenance and gain) and costs (the total oxygen consumption of the animal). It is hypothesized that, for the animal, the intake level where the ratio between benefits and costs becomes maximal, is optimal. Predictions of this optimum level for a wide range of feeds are shown to agree closely with observed voluntary feed intake in non-reproducing ruminants. Physiological processes related to the concept of an optimum feed intake are discussed. Maintenance of intracellular pH and associated energy costs may appear to be key factors in view of increases of the metabolic acid load consequent upon changes in intake. It is concluded that the concepts developed here may reflect a more universal principle governing the intensity of different forms of behaviour in ruminants as well as in monogastric animals.Part II reports results of a long-term feeding experiment with small West African Dwarf goats and a larger sheep breed given pelleted roughage. Between species, intake of digestible organic matter and fasting heat production appeared to vary as a function of metabolic weight.The effect of nutrient supplements on intake of low to medium quality roughages was investigated in supplementation and infusion experiments with the same species. Nutritive substances tested were by-pass protein, rumen microbial material, grass juice, intestinally digestible carbohydrates, and volatile fatty acid mixtures. Nutrient supplements usually depressed roughage intake but increased estimated intake of metabolizable energy (ME). From the theory presented in Part I it is inferred that such changes of intake are the result of changes of the efficiency of ME utilization.
A Dairy Farming Model was developed to screen the potentials for development of dairy farming on sandy soils in the Netherlands with respect to environmental, agro-technical and economic demands. The Dairy Farming Model consists of technical coefficient generators (TGC models) and an interactive multiple goal linear programming model (IMGLP model). The TCG models have been used to quantify input-output coefficients for a wide range of production techniques for grass, maize, fodder beet and milk. The results of the TCG models have been used in the IMGLP model, that optimizes the set of production techniques with respect to the goals defined.The model has been applied to a fictitious region with sandy soils. The analysis shows that dairy farming can meet both economic and environmental goals, as set by the government for the year 2000. However, this requires a reduction in labour income. Many different dairy farming systems are possible. A few general characteristics are: low N application on grazed grassland, a large proportion of the animals housed in low-emission stables and a substantial part of the concentrates produced in the region itself,Application of the Dairy Farming Model to the situation at the experimental dairy farm 'De Marke' has shown that the model is suited for exploring the opportunities for the development of dairy farming at a specific location, provided it can be initialized for that situation. Initial farm lay-out and measures taken at 'De Marke' have been evaluated.
Definitions and concepts of production ecology are presented as a basis for development of alternative production technologies characterized by their input-output combinations. With these concepts the relative importance of several growth factors and inputs is investigated to explain actual yield levels and resource-use efficiencies. Differences between potential and actual levels are analyzed to open ways for improved production technologies. The basis of the analysis is knowledge of basic physical, chemical, physiological and ecological processes at soil, field and crop level. New production technologies and their input-output combinations can be used in studies aimed at the exploration of options for sustainable agricultural production systems and land use. The concepts allow a systematic analysis and quantification of input-output combinations and clearly discriminate between bio-physical possibilities and socio-economic constraints and objectives. They help in defining objectives and means for agricultural production and land use, and may be valuable as aids to communication between various disciplines involved in studying the possibility and feasibility of future production technologies and land use options. The concepts production level, physical environment, target-oriented approach, production technique, production activity, and production orientation are applied to identify new technologies and production systems at various levels of scale, each requiring different types of information. In this paper some examples of applications are given at field, farm and at regional level.
Hunger is a physiological and psychological state resulting in the initiation of feeding; satiety, the opposite state, results in the termination of feeding. Appetite is used to describe a specific hunger drive and is not used here in the more general sense. Palatability, although rejected by some investigators as of limited value in studies with ruminants, is employed here with reference to the overall sensory impression received by the animal from its feed. This is in keeping with the statement of Young that it is 'the hedonic response of an organism to a foodstuff depending on its taste, smell, flavor, temperature, texture, feel, appearance, surroundings, and related conditions'. Evidence that energy balance is regulated is also presented. The short term control of intake by meal size and frequency is then considered, followed by a discussion of the ways in which information on the state of energy balance is relayed to the areas of the central nervous system that control feeding; work on the characterization of these centers is discussed. 440 references are given.
In the industrialized countries dramatic decreases in the number of people employed in agriculture have been made possible by a rise in soil and labour productivity. There is scope for these to improve further, particularly in developing countries. Potential yields are determined by the characteristics of the crop, local temperature and sunlight. Because the availability of nutrients and that of water are limiting for at least part of the growing season in most agricultural lands, attainable yields are lower than potential yields. Proper management of nutrient inputs, such that optimum use is made of each, can reduce this gap without causing negative environmental side-effects. Actual yields are lower than attainable yields because of growth-reducing factors, such as pests, diseases and weeds. For sustainable agriculture these should be controlled mainly by biological measures. There are many possibilities for this, thus biocides may be used as a last resort not as preventive insurance. Potential yields of rice and sugarcane can reach 30,000 kg ha-1 per year of consumable organic matter, sufficient to feed 120 people. Such yields cannot be achieved on all agricultural land, but it is estimated that world food production could support a population of 80 thousand million, if they were all vegetarian and required only 1500 m2 for non-food-related purposes. The green revolutions that occurred in the Western industrialized countries in the late 1940s and early 1950s and in Asia in the late 1960s and early 1970s need to be followed by a similar increase in agricultural productivity in Africa and West Asia to feed their rapidly growing populations. Better use of fertilizers and good water management require well-educated farmers with the financial means to implement long-term strategies. If these developments are managed properly, food production for the ever-increasing human population can be guaranteed and the burden on the environment and natural habitats reduced, enabling the development of sustainable agricultural systems.
Agriculture in the European Community is going through a phase of accelerating changes that calls for major decisions. The continued increase in production per unit of land area and per unit of livestock, due to improved production circumstances, better cultivation methods and external inputs, has led to significant increases in agricultural productivity. Abundant use of fertilizers and pesticides in some regions has created considerable negative environmental side effects, whereas in other regions, under-use of external inputs has created environmental problems of another nature, such as erosion. Increase in productivity per unit of area will continue during the coming decades, as the gap between potential and actual yields remains very big and the efficiency of use of external inputs is generally greater at high than at low production levels. However, the production surpluses in the EC create budgetary problems for the EC and a strong distortion of the world market for agricultural products. Decreasing prices of agricultural products create bankruptcy of farms in the rural structure and environment. The orientation of the Common Agriculture Policy and broadening of the objectives related to the rural environment need a clear and explicit formulation of options and a study of the possibilities and ways to achieve them. The Netherlands Scientific Council for Government Policy initiated such a study and developed various options for rural policy in which the preferences for a number of objectives related to agriculture and rural development are made explicit, and their consequences for land use are shown. Land use is chosen as the central theme because through changes in land use all other changes can be linked to each other. A qualitative and quantitative land evaluation is used to demonstrate the upper bounds for agricultural and forestry production.
Trajectories over time of nitrogen use and yield show that the fertilizer is used as efficiently at the high end of the yield range, as at the low end. Apparently, any decrease in marginal returns as predicted by the law of diminishing returns is more or less compensated by the benefits of other technological changes. Main processes that govern such opposing trends are analyzed in this paper to contribute towards more efficient use of resources in agriculture. The analyses elaborate on the optimum law of Liebscher, formulated at the end of the 19th century. This law states that a production factor which is in minimum supply contributes more to production, the closer other production factors are to their optimum. With some reservations regarding the control of pests, diseases and weeds, this law is fully confirmed. Accordingly, no production resource is used less efficiently and most production resources are used more efficiently with increasing yield level due to further optimizing of growing conditions. Whether external means of production are used at all depends of course on their price, but as soon as the farmer can afford them, they should be used in such a way that the production possibilities of all other available resources are fully exploited. It thus appears that with further optimizing of the growing conditions an increasing number of inputs gradually lose their variable character and the number of fixed operations on the farm increase. This makes more and more inputs not a variable cost element, but a complementary cost element of the decision to farm a piece of land. Therefore strategic research that is to serve both agriculture and its environment should not be so much directed towards the search for marginal returns of variable resources, as towards the search for the minimum of each production resource that is needed to allow maximum utilization of all other resources.
Placing Sahelian village land use in a linear context Agro-silvo-pastoral Land Use in Sahelian Villages, Advances in Geo-ecology 33 Resource use efficiency in agriculture
- De Ridder
- N Rheenen
- T Stroosnijder
- L Nibbering
- J W Stroosnijder
- L Rheenen
De Ridder, N., van Rheenen, T., Stroosnijder, L., Nibbering, J.W., 2001. Placing Sahelian village land use in a linear context. In: Stroosnijder, L., van Rheenen, T. (Eds.), Agro-silvo-pastoral Land Use in Sahelian Villages, Advances in Geo-ecology 33. Catena Verlag, Reiskirchen, Germany, pp. 309–327. G.W.J. van de Ven et al./Agricultural Systems 76 (2003) 507–525523 rDe Wit, C.T., 1992. Resource use efficiency in agriculture. Agricultural Systems 40, 125–151
The Biology of Agricultural Systems Agro-silvo-pastoral Land Use in Sahelian Villages Advances in Geo-ecology 33
- A P Spedding
- L Stroosnijder
- T Van Rheenen
Spedding, A.P., 1988. The Biology of Agricultural Systems. Academic Press, London. Stroosnijder, L., van Rheenen, T., 2001. Agro-silvo-pastoral Land Use in Sahelian Villages, Advances in Geo-ecology 33. Catena Verlag, Reiskirchen, Germany.
La production animale
- Ketelaars
Ketelaars, J.J.M.H., 1991. La production animale. In: Breman, H., de Ridder, N. (Eds.), Manuel sur les paˆ des pays saheí. ACCT-CTA-KARTHALA, Paris/Wageningen, pp. 357–388.
Yield estimation and agro-technical description of production systems Land Use Analysis and Planning for Sustainable Food Decurity: With an Illustration of Hasyana
- S K Bandyopadkyay
- H Pathak
- N Kalra
- P K Aggarwal
- R Kaur
- H C Joshi
- R Choudhary
- R P Roetter
Bandyopadkyay, S.K., Pathak, H., Kalra, N., Aggarwal, P.K., Kaur, R., Joshi, H.C., Choudhary, R., Roetter, R.P., 2002. Yield estimation and agro-technical description of production systems. In: Aggar-wal, P.K., Roetter, R.P., Kalra, N., van Keulen, H., Hoanh, C.T., van Laar, H.H. (Eds.), Land Use Analysis and Planning for Sustainable Food Decurity: With an Illustration of Hasyana, India. IARI/ IRRI/WUR, New Delhi/Los Banõ/Wageningen, pp. 61–89.
Plant, Animal, Man and Environment. A Course on the Biological Background and the Environmental Effects of Agricultural Production and on the Management of its Sustainability
- J J E Bessembinder
- R H Bosma
- N De Ridder
- S C De Vries
- C Kroeze
- D L Schuiling
- M A Slingerland
- G W J Van De Ven
- M K Van Ittersum
Bessembinder, J.J.E., Bosma, R.H., de Ridder, N., de Vries, S.C., Kroeze, C., Schuiling, D.L., Slingerland, M.A., van de Ven, G.W.J., van Ittersum, M.K., 2001. Plant, Animal, Man and Environment. A
Course on the Biological Background and the Environmental Effects of Agricultural Production and on
the Management of its Sustainability. Wageningen University, Wageningen. Available: http://
www.dpw.wageningen-ur.nl/pdmm.
An Introduction to Practical Animal Breeding Placing Sahelian village land use in a linear context
- D C Dalton
- Granada
- London
- N De Ridder
- T Van Rheenen
- L Stroosnijder
- J W Nibbering
Dalton, D.C., 1981. An Introduction to Practical Animal Breeding. Granada, London. De Ridder, N., van Rheenen, T., Stroosnijder, L., Nibbering, J.W., 2001. Placing Sahelian village land use in a linear context. In: Stroosnijder, L., van Rheenen, T. (Eds.), Agro-silvo-pastoral Land Use in Sahelian Villages, Advances in Geo-ecology 33. Catena Verlag, Reiskirchen, Germany, pp. 309–327.
Handboek Melkveehouderij (Manual for dairy farming)
- I Vink
- H Wolbers
Vink, I., Wolbers, H., 1997. Handboek Melkveehouderij (Manual for dairy farming). Praktijkonderzoek
Rundvee. Schapen en Paarden, Cabri, Lelystad.
Description quantitative dessystè mes de produc-tion animale en zone soudano-saheí. Rapports PPS No. 27 Control of feed intake and regulation of energy balance in ruminants
- References Bakker
- E J Hengsdijk
- H Ketelaars
References Bakker, E.J., Hengsdijk, H., Ketelaars, J.J.M.H., 1996. Description quantitative dessystè mes de produc-tion animale en zone soudano-saheí. Rapports PPS No. 27. Baile, C.A., Forbes, J.M., 1974. Control of feed intake and regulation of energy balance in ruminants. Physiological Reviews 54, 160–214.
Effects of cold environments on nutrient requirements of ruminants
- Young
Young, B.A., 1976. Effects of cold environments on nutrient requirements of ruminants. In: Fonnesbeck,
P.V., Harris, L.F., Kearl, L.C. (Eds.), Proceedings of the First International Symposium of Feed
Composition, Animal Nutrient Requirements and Computerization of Diets. Utah State University,
Logan, pp. 491-496.
Competing for Limiting Resources: the Case of the Fifth Region in Mali
- Van Soest
- P J F R Cisse´
- P A Van Duivenbooden
- N Van Keulen
Van Soest, P.J., 1982. Nutritional Ecology of the Ruminant. O&B Books, Corvalis. Veeneklaas, F.R., Cisse´, P.A., van Duivenbooden, N., van Keulen, H., 1991. Competing for Limiting Resources: the Case of the Fifth Region in Mali (Report 4). Development Scenarios. Centre for Agrobiological Research (CABO-DLO), Wageningen, The Netherlands.
Voedernormen voor de landbouwhuisdieren en voederwaarde van veevoeders: verkorte tabel
CVB, 1998. Voedernormen voor de landbouwhuisdieren en voederwaarde van veevoeders: verkorte tabel.
CVB, Lelystad.