Redirecting technology to support sustainable farm management practices

Agricultural technology has increased farm production to unprecedented levels. However, return on investment is diminishing and environmental concerns conflict with current input intensive farm practices. Conventional technologies and their application such as crop breeding and management practices have focused on monocultural systems that are dependent on chemical inputs to produce optimum yields. Current profit margins are low or non-existent with these conventional non-sustainable practices and must be changed if the family farm is to survive. We propose an ecologically based approach to farm management that strives to reduce reliance on chemically intensive inputs through better use of multiple attributes inherent within agroecosystems. This approach requires a redirection in the development and application of current and emerging technologies. Examples of redirections in research and development programs for pest management practices, genetic engineering, and precision agriculture necessary to provide a more ecologically-based and sustainable farming approach are illustrated.     

The role and value of eastern star clover in managing herbicide-resistant crop weeds: A whole-farm analysis

In the broadacre dryland farming system of Western Australia herbicide resistance in major crop weeds is an increasingly serious problem. A new option to combat herbicide resistance involves growing eastern star clover (Trifolium dasyurum). This is a new pasture legume with a unique delayed germination that allows control of weeds using various chemical and non-chemical strategies, without unduly compromising the pasture’s subsequent production. This study assesses the role and value of eastern star clover in managing herbicide-resistant weeds on various farms. The study employs the farming system model known as MIDAS, a whole-farm, bioeconomic model. Key scenarios of different degrees of severity of herbicide resistance for three farm types are examined. The main findings of the analysis are that as the severity of herbicide resistance increases, eastern star clover becomes an increasingly attractive option. Although the introduction of eastern star clover does reduce a farm’s capacity to carry sheep, and thereby lessens profits generated by the sheep enterprise, it enables longer, more profitable sequences of crops to be grown with fewer weed problems. Sensitivity analysis suggests that reduced cost of eastern star clover seed, cheap supplementary feed, and higher grain prices will further increase the profitability of eastern star clover.     

Decision-making processes for crop management on African farms. Modelling from a case study of cotton crops in northern Cameroon

Recent work on decision processes on French farms and irrigated systems in Africa has shown that farmers plan their cyclical (recurrent) technical operations, and that one can model this planning process. Taking the case of cotton crop management in North Cameroon, we show that with certain adjustments, modelling of this kind can also be done for rainfed crop farming in Africa. The adjustments are needed to take account of the differences in social status between different fields on one farm and the implications of the fact that farm work is primarily manual. This produces decision models with a similar structure to that described for technical management of an annual crop break in a temperate climate using mechanised implements. Not only do these models give us a detailed understanding of the variability of farming practices, we can also classify them into categories according to weather scenarios yield level as a function of weather scenario. We show that one can attribute farms to these types of model using simple indicators concerning work organisation. By analysing North Cameroon farmers' decision processes for managing cotton crops we can thus produce an effective tool for organising technical supervision of farmers at the regional level: advisers can work with these decision model types by measuring some simple indicators at farm level to predict which types of model are applicable, without the onerous work of constructing individual decision models.     

Characterising rice-based farming systems to identify opportunities for adopting water efficient cultivation methods in Tamil Nadu, India

Efficient water use in rice cultivation is a prerequisite for sustaining food security for the rice consuming population of India. Novel rice production practices, including water-saving techniques, modifications in transplanting, spacing, weeding and nutrient management, have been developed and shown to be effective on farm, but adoption of these techniques by farmers has remained restricted. Potential constraints include technical difficulties with new practices, and labour and gender issues which differ between farms. On the basis of a rapid survey of 100 rice-based farms, four farm types were identified based on their socio-economic and biophysical characteristics. Detailed farm surveys were conducted on three representative farms of each farm type to evaluate land use patterns, use of inputs such as water, labour, nutrient, capital and machinery, income from crop and animal production and off-farm activities. Opportunities exist for one or more new rice cultivation techniques to be adopted in all the four farm types. For all farm types, however, the opportunities for use of water-saving irrigation were the least promising. In general, adoption of water-saving irrigation will not improve farmers’ livelihoods despite its importance in reducing water scarcity problems at regional scale. At farm scale, the potential for adoption of water-saving irrigation depends on the season, location of fields and the irrigation source. Changes in government policies such as rules and regulations, pricing, institution building and infrastructure development, as well as training and education of farmers are needed to improve the adoption of modified methods for rice cultivation.     

Large-scale simulation of wheat yields in a semi-arid environment using a crop-growth model

The ability to predict wheat yields from large-scale weather variables has benefits throughout the semi-arid regions of the world. In spite of the availability of numerous crop-growth models, there has been little concerted effort to analyse yields regularly at spatial scales that are relevant to agronomic decision makers. As a result many current crop-growth models are research tools only. A large-scale wheat yield assessment procedure, based on the CERES Wheat model, has been developed for the semi-arid climate of Saskatchewan. It is suitable for simulating yields at the crop-district level, an area of about 2 million hectares containing several hundred farms having different soils, climates and management practices. Simulations of spring wheat growth, using this procedure, have revealed two critical periods (vegetative and ear growth) when lack of moisture has the greatest impact on grain yields. Knowledge of these times could be useful in devising early warning programmes for drought amelioration, combined with reliable long-term climate forecasts. Decisions made during these critical periods would affect farm management, marketing strategy and planning for the next growing season. ©     

大田无人农场关键技术研究现状与展望

无人农场是智慧农业的一种表现形式,也是建设农业强国和实现农业现代化的重要探索。无人农场以数据、知识和智能装备为核心要素,将现代信息技术与农业深度融合,实现农业全过程生产所需的信息感知、定量决策、智能控制、精准投入及个性化服务一体化。本文系统地阐述了大田无人农场的概念与总体技术架构,讨论了信息感知与智能决策、精准作业系统与装备、自动驾驶、无人化农机装备以及无人农场管控平台等五项大田无人农场的关键技术与装备,深入分析了发展中国大田无人农场亟待解决的关键科学与技术问题。以吉林省公主岭市玉米无人农场为例介绍了物联网、大数据、云计算以及人工智能等技术在玉米全程无人化生产中的具体应用及效果。最后,展望了无人农场在解决全球农业生产面临的“无人种田”等共性问题中发挥的重要作用,分析了中国发展无人农场存在的机遇和挑战,提出了中国发展无人农场的战略目标与思路。     

Use of available information at a European level to construct crop nitrogen response curves for the regions of the EU

Nowadays European agriculture is evolving in a context where policy-making and environmental concerns play a key role. To better assess agro-environmental policies, the AROPAj agricultural supply model needs to take into account the technical characteristics of crop management for different farms. A method to build up specific relationships between yield and nitrogen fertilization that takes into account agronomic techniques is proposed in this paper. The nitrogen response curve is based on an exponential function that integrates economic properties consistently from an agronomic point of view. In AROPAj, individual production systems (farm types) do not have a given location within a specified region and in databases technical information is scarce. The method involves determining technical and physical characteristics, inputs that allow the STICS crop model to assess the yield response to nitrogen of each crop on every farm type. From this information, a nitrogen response curve can be drawn up for each crop of each one of the farms. It can take into account both nitrogen from purchased fertilizer and nitrogen from animal effluents produced on farm. The method was designed to be adaptable to any European region, and tests carried out on two French regions covering a wide range of situations (crops, soils, climates and techniques) showed it was able to cope with varying prices and environments. The agronomic consistency of STICS inputs and curve shapes was also checked. When incorporated into the AROPAj economic model, the response curves can be used to render farms more sensitive to agricultural policy scenarios, by allowing their optimal fertilization level to be adjusted.     

Modelling the fate of water and nitrogen in the mixed vegetable farming systems of northern Tasmania, Australia

A combination of high input management systems, high annual rainfall and deep, permeable soils in northern Tasmania create conditions that are conducive to high drainage and nitrogen losses below the root zone. An understanding of the extent and mechanism of such losses will enable farm managers and their consultants to identify and implement more sustainable management practices that minimise potential adverse financial and environmental consequences. Analysing the fate of water and nutrients in farming systems is complex and influenced by a wide range of factors including management, soil characteristics, seasonal climate variability and management history of the paddock/farm in question. This paper describes a novel farm system modelling approach based on the model APSIM, for analysing the fate of nitrogen and water in mixed vegetable-based farming enterprises. The study was based on seven case farms across the Panatana catchment in northern Tasmania. Substantial simulated drainage losses (>100 mm average seasonal loss) were apparent for all crop and rotation elements across all farms in response to the surplus between crop water supply and crop water use. Crop nitrogen demand was found to be close to crop nitrogen supply for all crop and pasture rotation elements with the exception of potato, which had an average surplus nitrogen supply of 89 kg N/ha. This resulted in potato having much higher nitrate nitrogen leaching losses (32 kg N/ha) compared to other crops (<10 kg N/ha). Simulations suggest that practicable management options such as deficit-based irrigation and reduced N fertiliser rates will maintain current levels of productivity while reducing potential offsite N loss and generating significant financial savings via reduced input costs.     

Assessment of the quality of farmers’ environmental management and its effects on resource use efficiency: a Dutch case study

Most research on efficiency of farm management focuses on the relationship between an array of individual management variables and technical performance. Few studies have analysed the relationship between the total complex of farm management and technical farm performance. The present study uses the concept of strategic management and applies it to nitrogen management on Dutch arable farms. Data Envelopment Analysis (DEA) was used to assess farm specific efficiency scores for fertiliser use. For a sub-sample of the farms used in the DEA analysis, the strategic management concept was addressed by means of a workshop. The management elements (objectives, internal analysis of weaknesses and strengths, external analysis of opportunities and threats, and synthesis) were made operational by relating them to the introduction of the Mineral Accounting System (MINAS) by 2001 for Dutch arable farms. The external analysis was evaluated by questions about the MINAS rules; the internal analysis was evaluated by questions on the expected consequences for the farmer's N management. An interactive simulation model evaluating whether farmers were able to choose the optimal fertilisation strategy in view of MINAS assessed the ‘quality of the synthesis’. A positive significant correlation was found between the ‘quality of the synthesis’ and N efficiency.     

Perceived environmental uncertainty in Dutch dairy farming: The effect of external farm context on strategic choice

This paper explores the way in which dairy farmers perceive their environment (PE), i.e., the external context of their farm, and the uncertainty (PEU) this poses to them. The environment is defined using the STEP concept (society, technology, economy and politics) and Porter’s five forces model. The relationship between the perception of the external farm environment and the strategy farmers choose for their farm is quantified to gain insight into the effect of the external farm environment on decision-making. Data from a survey of 103 Dutch dairy farmers was analyzed using regression analysis. The results indicate that environmental uncertainty is not related to complexity or dynamism, but to the illiberality (i.e., intolerance, hostility) of the external farm environment. The institutional environment is considered especially illiberal, thus causing high uncertainty. Farmers with high PEU are more likely to choose a diversification strategy, while low perceived uncertainty results in a process-control strategy for the farm. A growth strategy is not affected by perceived environmental uncertainty. The PE and PEU approach is new in agricultural research and shows that the farmer’s view on the external environment is a key issue for decision-making on farms The significant relationship between the perceived uncertainty caused by the external farm environment and farm strategies shows that to get a good understanding of the farm, farming system boundaries should be expanded to incorporate the effect of the external farm environment on decision making. Reduction of uncertainty will enhance decision-making because instead of farming within an uncertain context, risk management practices can be used.     

Soil management: The key factors for higher productivity in the fields utilizing the system of rice intensification (SRI) in the central highland of Madagascar

The system of rice intensification (SRI) developed in Madagascar in the 1980s has been promoted as an integrated crop and resource management approach to rice-cultivation, especially for resource-limited smallholder farms. While advocates have claimed that SRI could boost rice yields with less external input, many criticisms have challenged its effectiveness regarding yields and applicability to larger-scale rice farming systems. In this study, we conducted a field survey and on-farm experiments to assess rice yield performance and key management practices on a few of the early SRI-practicing smallholder farms in the central highland of Madagascar.Rice grain yields at the survey fields were 9.9 t ha⁻¹ maximum without using mineral fertilizer. Deep plowing to the depth of 25–30 cm as well as SRI practices have been conducted continuously since the early 1990s. In addition, an effective drainage system facilitated intensive water management at these high-yielding fields. On-farm experiments demonstrated some yield increases with no interaction for the examined SRI practices, though the effects were not great enough to explain the high yields at these fields. The soils of these high-yielding fields contained relatively large amounts of soil organic carbon (SOC) from the surface to the deep soil layers, and the soil mineralizable nitrogen was closely correlated with rice grain yields.The results indicated that the high yields at the fields of those who were early to adopt SRI were mainly due to the soil fertility associated with great nitrogen-supplying ability, rather than ‘synergetic effects’ of the SRI components. This high N-supplying ability of the soil and accumulated SOC from surface to deep soil layers were attributable to the long-term combined practices of extensive organic applications and deep plowing. Soil hydrology could be another key factor stimulating high rates of soil N-mineralization.These management practices were, however, only applied to the limited numbers of fields within less than 1.0 ha of total landholdings of these farmers due to the great demand in labor and organic resources and the difficulty in controlling irrigation water. Intensive weeding and widely spaced transplanting of young seedlings were also performed in the fields with irrigation and drainage systems sufficient to avoid yield losses from flooding and drought. Although extensive and long-term systematic research is further required to fully assess the benefits of this sort of intensive management as opposed to conventional methods, the preferential allocation of intensive management by the successful SRI-adopters might be the implication of its location-specificity and difficulty in scaling up even within the resource-limited smallholder farms.     

A model of decision-making and information flows for information-intensive agriculture

This paper describes the development of a systems based model to characterise farmers’ decision-making process in information-intensive practices, and its evaluation in the context of Precision Agriculture (PA). A participative methodology was developed in which farm managers decomposed their process of decision-making into brief decision statements along with associated information requirements. The methodology was first developed on a university research farm in Denmark and further revised during testing on a number of research and commercial farms in Indiana, USA. Twenty-one decision-analysis factors were identified to characterise a farm manager’s decision-making process. Then, a general data flow diagram (DFD) was constructed that describes the information flows “from data to decision”. Illustrative examples of the model in the form of DFDs are presented for a strategic, a tactical and an operational decision. The model was validated for a range of decisions related to operations by three university farm managers and by five commercial farmers practicing PA for cereal, corn and soybean production in Denmark and in Indiana, USA.     

Evaluation of the water use efficiency of alternative farm practices at a range of spatial and temporal scales: A conceptual framework and a modelling approach

Water is the principal limiting resource in Australian broadacre farming, and the efficiency with which farmers use water to produce various products is a major determinant both of farm profit and of a range of natural resource management (NRM) outcomes. We propose a conceptual framework based on multiple water use efficiencies (WUEs) that can be used to gain insight into high-level comparisons of the productivity and sustainability of alternative farming practices across temporal and spatial scales. The framework is intended as a data aggregation and presentation device. It treats flows of water, biomass and money in a mixed farming system; economic inefficiencies in these flows are tracked as they are associated with a range of NRM indicators.We illustrate the use of the framework, and its place in a larger research programme, by employing it to synthesise the results from a set of modelling analyses of the effect of land use choices on long-term productivity and a range of NRM indicators (frequency of low ground cover, deep drainage, N leaching rates and rate of change in surface soil organic carbon). The analyses span scales from single paddocks and years to whole farms and have been carried out with the APSIM and GRAZPLAN biophysical simulation models and the MIDAS whole-farm economic model.In single wheat crops in one study, different land uses in preceding years affect grain yield primarily by affecting the harvest index. When the scale changes to cropping rotations, the critical factor affecting overall water use efficiency is found to be the proportion of stored soil water that is transpired by crops. When ordinated in terms of their water use efficiencies, a set of 45 modelled rotation sequences at another location are differentiated mainly by the proportion of pasture in the rotation; when rotations are ordinated using key NRM indicators, the proportion of lucerne pasture is the main distinguishing factor. Finally, we show that for whole crop-livestock farms at three different locations across southern Australia, the pattern of water use efficiencies in the most profitable farming systems changes in similar ways as cropping proportion is altered. At this scale, land use choices affect multiple water use efficiency indices simultaneously and commodity prices determine the balance of the resulting economic tradeoffs.Limitations to the use of the WUE framework arising from its relative simplicity are discussed, as are other areas of farming systems research and development to which it can be applied.     

Evaluating crop-livestock-based farming systems: A village level study in the dry zone rainfed district of Sri Lanka

In Sri Lanka cropping enterprises interact with livestock production on peasant farms. This analysis of the crop-livestock farming system aims at understanding the existing constraints and interactions between crops and livestock in these farms. The main objective of the study is to describe the crop-livestock integrated farming systems in three rainfed villages in the Moneragala district of Sri Lanka, and to evaluate these systems in terms of maximizing farm incomes from the different crop and livestock components of the systems. A field survey was conducted to collect data from 153 farming families for the Maha season of 1982/83. A linear programming model was formulated to test the hypothesis.The results show that in general the activities for lowland rice, highland rice, sugar cane, labour, farm cash cost, and MVP (compost) are higher in the optimal farm plan than in the actual farm situation. The livestock in the optimal plan is mainly confined to milch cattle. However, with the present high level of manutrition among rural livestock industry emphasis should be placed on the expansion of the rural livestock industry in the study villages. The optimal plan also suggests the use of crop residues as a substitute for compost for farm crops. Hence, in the context of escalating prices of chemical fertilizers, research is required to find the suitability of crop residues and household residues as substitutes for compost. Increases in supplementary irrigation may result in the expansion of the farm area and hence the farm income. Other methods of increasing farm income include: replacing hired labour with non-utilized family labour, increased agricultural research, and extension activity regarding the use of modern inputs by farmers on crops.     

A mathematical programming model of a crop-livestock farm system

A whole-farm mathematical programming model has been built for dryland crop-livestock farms in Western Australia. The multidisciplinary approach used for model building is presented and the resulting model structure is described. It is a mixed integer programming model which represents, in some detail, the biological, technological and financial relationships of the farming system and stresses the interdependencies of enterprises. The model is used to investigate the extent to which positive interactions between different enterprises influence the optimal farm plan. The management issue considered is the division of land between crop and pasture production. It is concluded that, for the farming system considered, interactions do have an influence on profit and the optimal farm plan.     

Farm types for beef production and their economic success in a mountainous province of northern Vietnam

The objective of this study was to compare the management and economic success of beef production by three types of farm in northwestern Vietnam. The potential of household farms to supply beef for the market and their competition with large farms were examined.The fieldwork was done in 2007 on 73 farms consisting of 58 small mixed farms (small farms), 10 medium mixed farms (medium farms) and 5 specialised large-scale beef farms (large farms) in Son La province. The three types of farm differed in ethnicity (Thai, H’mong, and Kinh), remoteness (lowland, highland), production objectives (subsistence, market output), degree of specialization (mixed farm, specialised beef farm) and integration of production (single farmers, cooperative). Data on biological productivity, inputs and outputs, and the social contribution of cattle production were collected by household and key person interviews, participatory rural appraisal tools and cattle body measurements. Economic values were derived by assessment of market or replacement costs. Quantitative data analysis was done with linear models (PROC GLM) in the SAS software (version 9.1).Lowland small farms had higher costs for cattle production than the highland farms (0.8 Mill. VND head⁻¹ year⁻¹ compared with 0.02 Mill. VND head⁻¹ year⁻¹, respectively). The large farms had high production costs, with an average of 2.5-3.6 Mill. VND head⁻¹ year⁻¹. Cattle brought high benefits of non-cash values to the household farms. The total revenue from cattle was in the range 4.5-11.5 Mill. VND head⁻¹ year⁻¹, which depended on the use of non-market functions of cattle on the household farm. The value of net benefit/kg live weight (LW) of lowland small farms with an average of 39,000 VND/kg LW was significantly higher than that of the medium and small farms in the highlands (26,000 VND/kg LW). However, the small farms kept fewer cattle than the medium farms (average of 2-4 cattle/farm compared with 9 cattle/farm, respectively) because of forage and labour shortages and have no option to further develop cattle production. Keeping larger numbers of cattle based on available natural pasture brought high benefit from stock value as farm liquidity to only the medium farms. This was the most promising type of farm for future development of beef production, given its actual success and the availability of underutilised resources. Large-scale farms suffered high economic losses of 0.3-1.4 Mill. VND cattle⁻¹ year⁻¹, due to the lack of professional management, high feed costs and low animal performance, and showed no potential for developing cattle production.     

Managing the grazing landscape: Insights for agricultural adaptation from a mid-drought photo-elicitation study in the Australian sheep-wheat belt

Globally, and under uncertain climate conditions, the agricultural sector will need to feed more people without degrading the ecosystem services on which production depends. Eastern Australia, coming out of a decade of drought, is at the leading edge of this challenge. Measures to adapt agriculture to increasing climate variability are urgently sought. One particularly promising measure is an adaptive grazing decision-making practice called holistic management (HM), typically involving high-intensity, short-duration rotational grazing and the encouragement of pastures with low chemical input needs. Here, we use photo-elicitation to compare the landscape perceptions of HM graziers with those of more conventional graziers, based on their choice of photo targets and the stories those photographs elicited. During that process, HM graziers described their use of adaptive farm management techniques to gain outcomes for production and ecosystems alike, demonstrating a system-based understanding of their farms conducive to farming under increased climate variability. We conclude that HM grazing should be encouraged so as to adapt the industry to climate change. More widespread uptake of HM practices - for public benefit as well as personal - depends on incentives to reduce start-up costs and expand the instruction of HM principles, first targeting those with high adaptive capacity, and removing policies that delay adaptation.     

Heterogeneity of crop productivity and resource use efficiency within smallholder Kenyan farms: Soil fertility gradients or management intensity gradients?

The decrease in crop yields at increasing distances from the homesteads within smallholder farms of Sub-Saharan Africa (SSA) is normally ascribed to the existence of within-farm soil fertility gradients. Field observations also suggest that a large part of such variability is concomitantly caused by poor agronomy. To understand the interaction between soil fertility (S factors) and management decisions (M factors) affecting crop variability, we combined field research conducted in western Kenya (Vihiga, Kakamega and Teso districts; rainfall: 1600, 1800 and 1200 mm, respectively) with explorations using the simple dynamic crop/soil model for dynamic simulation of nutrient balances, previously tested for the region. Field measurements indicated within-farm differences in average maize grain yields of 48% (2.7 vs. 1.4 t ha⁻¹) in Vihiga and of 60% (1.5 vs. 0.6 t ha⁻¹) in Teso, between fields that were close and far from the homestead, respectively. Extreme values ranged widely, e.g. between 4.9 and 0.3 t ha⁻¹ for all the farms surveyed in Vihiga, where the average farm size was 0.6 ha. Maize grain yields tended to increase with increasing contents of soil C, total N, extractable P and exchangeable bases. However, the negative relationship between S factors and distance from the homestead was not as strong as expected, and yield variability was better explained by multiple regression models considering M factors such as planting date, plant density, resource use and weed infestation (40–60% across sites). Then, we analysed the variation in resource (cash, labour, N) use efficiency within farms of different resource endowments with the aid of the simulation model. N balances at plot scale varied from ca. +20 to −18 kg ha⁻¹, from −9 to −20 kg ha⁻¹ and from −16 to −18 kg ha⁻¹ for the different fields of the high, medium and low resource endowment case-study farms, respectively. Labour productivities ranged between ca. 10 and 38 kg grain man-day⁻¹ across field and farm types. The results indicate the need of considering within farm heterogeneity when designing soil fertility management interventions. Resource use efficiency was strongly affected by soil quality. As farmers invest more effort and resources in the more productive and less risky fields, the interaction between S and M factors leads to farmer-driven resource use efficiency gradients within smallholder farms.