Combining dynamic economic analysis and environmental impact modelling: Addressing uncertainty and complexity of agricultural development

In this study, the impacts of different agricultural policies on agricultural production and nutrient leaching from agricultural land are evaluated using the economic DREMFIA agricultural sector model and the field-scale nutrient transport model ICECREAM. DREMFIA simulates competitive markets of agricultural products and includes an evolutionary scheme of technology diffusion which explicitly considers farm investments, evolving farm size structure and technological change. The technology diffusion model allows self-enforcing patterns of technical change driven by the spread of information and farmers' knowledge related to different technological alternatives. Hence, the long-term changes in agriculture due to policy changes may be essentially larger than those predicted by traditional static equilibrium models. Larger potential for changes in production provides a larger perspective for evaluation of environmental impacts. The modelled variables in ICECREAM are nitrogen and phosphorus losses in surface run-off and percolation. The considered environmental effect is eutrophication of surface waters. In this paper, the modelling strategy will be presented and highlighted using two case-study catchments with varying environmental conditions and land use.     

Detecting war‐induced abandoned agricultural land in northeast Bosnia using multispectral,multitemporal Landsat TM imagery

The use of satellite technology by military planners has a relatively long history as a tool of warfare, but little research has used satellite technology to study the effects of war. This research addresses this gap by applying satellite remote sensing imagery to study the effects of war on land‐use/land‐cover change in northeast Bosnia. Although the most severe war impacts are visible at local scales (e.g. destroyed buildings), this study focuses on impacts to agricultural land. Four change detection methods were evaluated for their effectiveness in detecting abandoned agricultural land using Landsat Thematic Mapper (TM) data from before, during and after the 1992–95 war. Ground reference data were collected in May 2006 at survey sites selected using a stratified random sampling approach based on the derived map of abandoned agricultural land. Fine‐resolution Quickbird imagery was also used to verify the accuracy of the classification. Results from these analyses show that a supervised classification of the Landsat TM data identified abandoned agricultural land with an overall accuracy of 82.5%. The careful use of freely available Quickbird imagery, both as training data for the supervised classifier and as supplementary ground reference data, suggests that these methods are applicable to other civil wars too dangerous for researchers' fieldwork.     

Identification of ditches and furrows using remote sensing: application to sediment modelling in the Tana watershed,Kenya

Ridge-tillage is an agricultural practice where crops are planted on elevated ridges, with furrows in-between. Ridge-tillage has been shown to significantly reduce erosion from croplands, but data on the presence of ridge-tillage is sparse and challenging to collect at the landscape scale. Thus, water quality models often do not account for ridge-tillage in a spatially-explicit manner, potentially overlooking the important impacts of this practice. We have developed a novel method that exploits the spectral, radiometric and linearity shape characteristics to identify both drainage ditches and ridge-tillage furrows using remote sensing of 0.5 m satellite data. We applied the method to the Sasumua watershed in Kenya, where we had false positives in only 3% of randomly selected polygons, and we detected the majority of ditches in 59% of randomly selected polygons. We then assessed the potential value of including these data in sediment modelling, showing that representing these practices could reduce sediment export in the study area by roughly 80%. Being able to readily identify the presence of ditches and furrows could enable the development of more accurate water quality models, and help identify priority areas for intervention to improve water quality (and possibly crop yields) through changing agricultural practices or policies.     

Assimilation of satellite data into agrohydrological models to improve crop yield forecasts

This paper addresses the question of whether data assimilation of remotely sensed leaf area index and/or relative evapotranspiration estimates can be used to forecast total wheat production as an indicator of agricultural drought. A series of low to moderate resolution MODIS satellite data of the Borkhar district, Isfahan (Iran) was converted into both leaf area index and relative evapotranspiration using a land surface energy algorithm for the year 2005. An agrohydrological model was then implemented in a distributed manner using spatial information of soil types, land use, groundwater and irrigation on a raster basis with a grid size of 250 m, i.e. moderate resolution. A constant gain Kalman filter data assimilation algorithm was used for each data series to correct the internal variables of the distributed model whenever remotely sensed data were available. Predictions for 1 month in advance using simulations with assimilation at a regional scale were very promising with respect to the statistical data (bias = ±10%). However, longer‐term predictions, i.e. 2 months in advance, resulted in a higher bias between the simulated and statistical data. The introduced methodology can be used as a reliable tool for assessing the impacts of droughts in semi‐arid regions.     

Agro-ecosystem modeling can aid in the optimization of biomass feedstock supply

Recent European Directives promoted the development of biofuels, requesting mandatory limits to their emissions ot greenhouse gases (GHG). Second-generation biofuels based on lignocellulosic biomass are prime candidates but their GHG emissions are variable and uncertain. Agro-ecosystem modeling can capture them and the performance of biofuel feedstocks.This study aimed at optimizing feedstock supply for a bioethanol unit in France, from agricultural residues, annual and perennial crops. Their productivity and environmental impacts were modelled on a regional scale using geo-referenced data on soil properties, crop management, land-use and future weather data. Several supply scenarios were tested. Cereal straw was the most efficient feedstock but had a low availability, and only miscanthus could meet the bioethanol plant's demand. Sorghum combined poor yields and high GHG emissions compared by miscanthus and triticale. A mix of three biomass sources used less than 3% of the regional agricultural land while abating GHG emissions by 60%.     

A work measurement method for application in Indian agriculture

The usual scales of rating in work measurement practices are based on subjective assessment of effort and walking speed of workers. In the present paper an attempt was made to correlate walking speed and stride length with selected anthropometric and physiological measurements (heart rates), using three groups of farm workers. Linear simple and multiple regressions were obtained to identify the best possible correlation between the above parameters. Accordingly, a rating scale was developed and applied in ploughing, transplanting, reaping and sheafing operation in rice cultivation.

The result showed that the preferred speed of walking of the farm workers was 1.39 m/s and step frequency at that speed was 103 steps/min. On application of the suggested scale on the abovee four agricultural operations, it was observed that the majority of the workers' working loads and efforts were more than they should be.     

Land cover characterization and mapping of continental Southeast Asia using multi-resolution satellite sensor data

Land use/land cover change, particularly that of tropical deforestation and forest degradation, has been occurring at an unprecedented rate and scale in Southeast Asia. The rapid rate of economic development, demographics and poverty are believed to be the underlying forces responsible for the change. Accurate and up-to-date information to support the above statement is, however, not available. The available data, if any, are outdated and are not comparable for various technical reasons. Time series analysis of land cover change and the identification of the driving forces responsible for these changes are needed for the sustainable management of natural resources and also for projecting future land cover trajectories. We analysed the multi-temporal and multi-seasonal NOAA Advanced Very High Resolution Radiometer (AVHRR) satellite data of 1985/86 and 1992 to (1) prepare historical land cover maps and (2) to identify areas undergoing major land cover transformations (called ‘hot spots’). The identified ‘hot spot’ areas were investigated in detail using high-resolution satellite sensor data such as Landsat and SPOT supplemented by intensive field surveys. Shifting cultivation, intensification of agricultural activities and change of cropping patterns, and conversion of forest to agricultural land were found to be the principal reasons for land use/land cover change in the Oudomxay province of Lao PDR, the Mekong Delta of Vietnam and the Loei province of Thailand, respectively. Moreover, typical land use/land cover change patterns of the ‘hot spot’ areas were also examined. In addition, we developed an operational methodology for land use/land cover change analysis at the national level with the help of national remote sensing institutions.     

Combining farm and regional level modelling for Integrated Resource Management in East and South-east Asia

Currently, in many of the highly productive lowland areas of East and South-east Asia a trend to further intensification and diversification of agricultural land use can be observed. Growing economies and urbanization also increase the claims on land and water by non-agricultural uses. As a result, decisions related to the management and planning of scarce resources become increasingly complex. Technological innovations at the field/farm level are necessary but not sufficient – changes in resource use at regional scale will also be essential. To support decision-making in such situations, we advocate a multi-scale modelling approach embedded in a sound participatory process. To this end, the Integrated Resource Management and Land use Analysis (IRMLA) Project is developing an analytical framework and methods for resource use analysis and planning, for four sites in Asia. In the envisaged multi-scale approach, integration of results from field, farm, district and provincial level analysis is based on interactive multiple goal linear programming (IMGLP), farm household modelling (FHM), production ecological concepts and participatory techniques. The approach comprises the following steps: (i) inventory/quantification of current land use systems, resource availability, management practices and policy views, (ii) analysis of alternative, innovative land use systems/technologies, (iii) exploration of the opportunities and limitations to change resource use at regional scale under alternative future scenarios, (iv) modelling decision behaviour of farmers and identification of feasible policy interventions, and (v) synthesis of results from farm to regional level for negotiation of the most promising options by a stakeholder platform. In the current paper, the operationalisation of dual-scale analysis is illustrated by the outputs (development scenarios, promising policy measures and innovative production systems) from various component models for the case study Ilocos Norte, Philippines. An approach is discussed for the integration of results from the different model components at two different decision making levels (farm and province).     

Multi-scale land-use disaggregation modelling: Concept and application to EU countries

Changes of carbon stocks in agricultural soils, emissions of greenhouse gases from agriculture, and the delivery of ecosystem services of agricultural landscapes depend on combinations of land-use, livestock density, farming practices, climate and soil types. Many environmental processes are highly non-linear. If the analysis of the environmental impact is based on data at a relatively coarse-scale (e.g. farm, country, or large administrative regions), conclusions can be misleading. For an accurate assessment of agri-environmental indicators, data of agricultural activities and their dynamics are needed at high spatial resolution. In this paper, we develop and validate a spatial model for predicting the agricultural land-use areas within the homogenous spatial units (HSUs). For the EU-28 countries, we distinguish about 1.5 × 10⁵ HSUs and we consider 30 possible land-uses to match with the classification used in the Common Agricultural Policy Regionalized Impact (CAPRI) model. The comparison of model predictions with independent observations and with a simple rule-based approach at HSU level demonstrates that the predictions are generally accurate in more than 75 % of HSUs. The frequent crops or land-use are better predicted. For non-frequent crops and/or crops requiring specific cultivation conditions, the model needs further fine-tuning.     

Pedal power for occupational activities: Effect of power output and pedalling rate on physiological responses

Because of the socio-economical conditions of farmers in developing countries including India, human muscle power is going to contribute energy requirements for performing many farm activities for the next two decades. Pedalling is the most efficient way of utilising power from human muscles. Pedal power enables a person to drive devices at the same or higher rate as that achieved by hand cranking, but with far less effort and fatigue. However, the use of pedal power for occupational work such as stationary farm operations has got scant attention in the past. Keeping these points into consideration a study was planned to optimise power output and pedalling rate for stationary farm operations. Physiological responses of 12 male subjects were studied on a computerised bicycle ergometer at five levels of power output (30-90 W) and seven levels of pedalling rates (30-90 rev min⁻¹). Analysis of data indicated that physiological responses were significantly affected with power output as well as pedalling rate. Increase in physiological responses (heart rate and oxygen consumption rate) over rest (delta values) were significantly higher when pedalling frequency was 30 rev min⁻¹ and above 50 rev min⁻¹. There was no significant difference between physiological responses at 40 and 50 rev min⁻¹. Physiological responses increased linearly with power output and were significantly different at different power outputs. The delta values of physiological responses at 60 W power output and 50 rev min⁻¹ pedalling rate (ΔHR = 40 beats min⁻¹ and ΔVO₂ = 0.56 l min⁻¹) were within acceptable limits for continuous pedalling work. From the results of the study it was concluded that for daylong pedalling work the power output from an Indian agricultural worker should be limited to 60 W and pedalling rate should be 50 rev min⁻¹.

Relevance to industry

The optimal power output and pedalling rate may be used for the ergonomic design of a dynapod or any pedalling device for efficient utilization of human muscle power with reduced drudgery and fatigue.     

A stochastic recursive interactive programming model for farm firm policy analysis

A first-generation, farm level recursive interactive programming model for analyzing the impacts of commodity farm programs on typical farms (FLIPRIP) is described and demonstrated. FLIPRIP uses a specialized linear programming algorithm (XMP) and the farm managementpolicy components of FLIPSIM. A Texas cotton farm is analyzed using stochastic crop prices and yields under two farm policy options to demonstrate the type of policy data generated by FLIPRIP.     

Assimilating satellite imagery and visible–near infrared spectroscopy to model and map soil loss by water erosion in Australia

Soil loss causes environmental degradation and reduces agricultural productivity over large areas of the world. Here, we use the latest earth observation data and soil visible–near infrared (vis–NIR) spectroscopy to estimate the factors of the Revised Universal Soil Loss Equation (RUSLE) and to model soil loss by water erosion in Australia. We estimate rainfall erosivity (R) using the Tropical Rainfall Measuring Mission (TRMM); slope length and steepness (L and S) using a 3-arcsec Shuttle Radar Topography Mission (SRTM) digital elevation model; cover management (C) and control practice (P) using the national dynamic land cover dataset (DLCD) of Australia derived from the moderate-resolution imaging spectroradiometer (MODIS); and soil erodibility (K) using vis–NIR estimates of the contents of sand, silt, clay and organic carbon in Australian soil. We model K using a machine-learning algorithm with environmental predictors selected to best capture the factors that influence erodibility and produced a digital map of K. We use the derived RUSLE factors to estimate soil loss at 1-km resolution across the whole of Australia. We found that the potential gross average soil loss by water erosion in Australian is 1.86 t ha⁻¹ y⁻¹ (95% confidence intervals of 1.78 and 1.93 t ha⁻¹ y⁻¹), equivalent to a total of 1242 × 10⁶ tonnes of soil lost annually (95% confidence intervals of 1195 and 1293 t × 10⁶ y⁻¹). Our estimates of erosion are generally smaller than previous continental estimates using the RUSLE, but particularly in croplands, which might indicate that soil conservation practices effectively reduced erosion in Australia. However we also identify localized regions with large erosion in northern Australia and northeastern Queensland. Erosion in these areas carries sediments laden with nitrogen, phosphorus and pollutants from agricultural production into the sea, negatively affecting marine ecosystems. We used the best available data and our results provide better estimates compared to previous assessments. Our approach will be valuable for other large, sparsely sampled areas of the world where assessments of soil erosion are needed.     

Mapping population density distribution at multiple scales in Zhejiang Province using Landsat Thematic Mapper and census data

Population density is usually calculated from the census data, but it is dynamic over time and updating population data is often challenging because it is time-consuming and costly. Another problem is that population data for public use are often too coarse, such as at the county scale in China. Previous research on population estimation mainly focused on megacities due to their importance in socio-economic conditions, but has not paid much attention to the township or village scale because of the sparse population density and less importance in economic conditions. In reality, population density in townships and villages plays an important role in land-use/cover change and environmental conditions. It is an urgent task to timely update population density at the township and cell-size scales. Therefore, this article aims to develop an approach to estimate population density at the township scale and at a cell size of 1 km by 1 km through downscaling the population density from county to township and then to cell size. We estimated population density using Landsat Thematic Mapper (TM) and census data in Zhejiang Province, China. Landsat TM images in 2010 were used to map impervious surface area (ISA) distribution using a hybrid approach, in which a decision tree classifier was used to extract ISA data and cluster analysis was used to further modify the ISA results. A population density estimation model was developed at the county scale, and this model was then transferred to the township scale. The population density was finally redistributed to cell-size scale based on the assumption that population only occupied the sites having ISA. This research indicates that most townships have residuals within ±50 persons/km² with a root mean squared error (RMSE) of 71.56 persons/km², and a relative RMSE of 27.6%. The spatial patterns of population density distribution at the 1 km² cell size are much improved compared to the township and county scales. This research indicates the importance of using the ISA for population density estimation, where ISA can be accurately extracted from remotely sensed data.     

Airborne thermal data for evaluating the spatial distribution of actual evapotranspiration over a watershed in oceanic climatic conditions—application of semi-empirical models

Considerable work has been done on estimating daily fluxes at the regional scale from thermal data acquired in the early afternoon using semi-empirical models. But these studies have dealt only with clear days and homogeneous surfaces. We evaluate the use of some of these models under variable climatic conditions. A campaign of micrometeorological measurements performed from May to October 1993 on a small agricultural catchment in Brittany (NW France) allowed us to monitor the water budget and fluxes of three characteristical surfaces (meadow, wheat and stubble field). Thermal images covering the whole catchment were acquired on 7 August 1993 using an infrared camera aboard a small aircraft. They were corrected for atmospheric effects with the LOWTRAN 7 model. After geometrical corrections to superimpose these images on a digitilized land use map, a mapping of surface fluxes at the catchment scale is proposed and discussed.     

Improved Fuzzy Rule Promotion-Based Localized Crop Knowledge Dissemination System for Farmers of Punjab (India) Using Mobile Phone Technology

Indian economy includes the major contribution provided by agriculturally driven activities. There is need to provide the agricultural stakeholders an appropriate, reliable, local, fast, ‘round-the-clock, and precise knowledge, possibly in their native language, in order that they can enhance their decision-making capacity to improve the agricultural productivity. This can be achieved by using mobile phones. Mobile phones are becoming part of the daily routine of farmers and can be used to disseminate this appropriate, multilingual, localized knowledge right at the location of use. In this article, an improved fuzzy rule promotion-based technique to infer advice to farmers has been presented. Using this technique, an Android-based mobile phone application named mAgIDS has been developed. This application uses a Global Positioning System (GPS) for localized inference and local language selected by the user. The application was tested on the dataset of a disease diagnosis of a paddy crop. The chi-square (χ²) test was used to match the result of the mAgIDS system with the experts’ inferences. It was found that the results of the system and experts match with an accuracy of 95.84% at a 5% level of significance. Twenty-two scientists, working at various locations within Punjab state in India, were contacted for validation of the system, with 15 relevant parameters. It was found that the proposed system is statistically valid. The real-time implementation of the system provides immense and timely help to the farmers in making appropriate decisions for their crop production.     

The application of a binary division procedure to the classification of forest subcategories using MODIS time-series data during 2000–2010 in China

Forests account for more than 23% of China’s total area. As the most important terrestrial ecosystem, forests have tremendous ecological value. However, it remains difficult to classify forest subcategories at the national scale. In this study, a newly developed binary division procedure was used to categorize forest areas, including their spatiotemporal dynamics, during the period 2000–2010. Time-series images acquired using the Moderate Resolution Imaging Spectroradiometer (MODIS), together with auxiliary data on land use, climate zoning, and topography, were utilized. Hierarchical classification and zoning were combined with remote-sensing auto-classification. Based on the forest extent mask, the state-level forest system was divided into four classes and 18 subcategories. The method achieved an acceptable overall accuracy of 73.1%, based on a comparison to the sample points of China’s fourth forest general survey data set. In 2010, the total forest area was 1.755 × 10⁶ km², and the total area of and shrubs was 4.885 × 10⁵ km². The total area of woodland increased by 2536.25 km² during the decade 2000–2010. The shrub subcategories exhibited almost no change during this time period; however, significant changes in forest area occurred in the mountainous region of Northeast China as well as in the hilly regions of Southern China. The main transformations took place in cold-temperate and temperate mountainous deciduous coniferous forest, subtropical deciduous coniferous forest, subtropical evergreen coniferous forest, and temperate and subtropical deciduous broadleaved mixed forests. The binary division procedure proposed herein can be used not only to rapidly classify more forest subcategories and monitor their dynamic changes, but also to improve the classification accuracy compared with global and national land-cover maps.     

Spatio-temporal distribution of burned areas by ecoregions in Mexico and Central America

Fire activity in Mexico and Central America, and its associated emissions, has impacts across multiple scales. On the local-to-regional scale, fire activity impacts land use, productivity, and biodiversity. On the regional-to-global scale, fire activity impacts hydrological, biogeochemical, and atmospheric processes. A consistent, reliable, large-scale characterization of the spatial and temporal distribution of fire burned area is required to assess its environmental impacts and to support natural resources’ management. The spatial and temporal distributions of fire burned areas in ecoregions of Mexico and Central America are evaluated in this study for the period 2001–2014, using the satellite Moderate Resolution Imaging Spectroradiometer (MODIS) MCD45 Burned Area data set. The methodology combines the 500 m burned area product with a MODIS land cover product and a map of North American land cover to calculate the spatiotemporal variability of fire activity as a function of land-use type.

The total burned area over Mexico and Central America over the period 2001–2014 was found to be 614,243.5 km², but with significant interannual variability over the 14 years included in the study. Indeed, the minimum burned area over the period was 9892.25 km² in 2014 and the maximum was 37,669.50 km² in 2011, a fourfold increase. Burned areas were found to be concentrated in northern Mexico and on the Pacific coast, mainly from October to June. Agricultural burned areas accounted for 37% and 43% of total detected burns in Mexico and Central America, respectively. The largest extent of burned surface occurs in May for most land-cover types. The maximum density of burned areas occurred in the tropical dry forests ecoregion during the dry season. Both in Mexico and Central America, burned area anomalies have significant anti-correlation with precipitation anomalies.     

Estimating the area of stubble burning from the number of active fires detected by satellite

To estimate the area affected by stubble burning in southern Australia, use was made of observations from the MODIS (MODerate resolution Imaging Spectroradiometer) on the Terra and Aqua satellites. The burnt area (BA) was calculated from the number of active fires, known as fire hot spots (FHS) using parameters estimated from a survey of farms in the agricultural area of south-western Australia. The study also served as a ground validation of the capability and limitations of the MODIS sensor and associated algorithm for detection of small agricultural fires.During the period from 1 March to 23 May 2005, 3240 unique FHS associated with stubble burning were detected. The majority of these FHS occurred in the afternoon in the last 3 weeks of April. To estimate the total area of stubble burning associated with these FHS, a survey was sent to 2066 farmers. This survey determined for each farm, the number of fields in which stubble was burnt, average size of field burnt (A), crop types burnt, dates and time of day of burning. Responses were received from 273 farms, 38% of whom reported over 500 stubble burns. The 3240 FHS were intersected with the polygons of the farm boundaries to determine the proportion (P) of stubble burns detected using MODIS. Only 13% (± 3%) of the stubble burns recorded in the farm survey were detected. Average field size burnt was 75 ha (± 6 ha). Total BA was calculated as: BA = A ^? 3240/P, which gave an estimated area of 1.87 million ha. This area was 27% of the total cropped area in south-western Australia. This level of stubble burning was similar to that determined by a 1990s survey in the State of Victoria.Neither cloud cover nor field size was significantly correlated with the low number of stubble fires that were detected. Therefore it was concluded that many stubble burns went undetected because of the lack of coincidence between the time of the MODIS overpass and when stubble burns were initiated. Also the use of additional sensors such as the Advanced Very High Resolution Radiometer (AVHRR) on NOAA satellites with afternoon overpasses would improve the fraction of stubble burns detected.Across the whole of Australia where winter cropping occurs, there was a high correlation (r² = 0.96) between FHS and total cropped area in each State. This provided the basis for extrapolating the field results from south-western Australia, to estimate the total area of stubble burning in southern Australia for 2005.     

Modelling Australian land use competition and ecosystem services with food price feedbacks at high spatial resolution

In a globalised world, land use change outlooks are influenced by both locally heterogeneous land attributes and world markets. We demonstrate the importance of high resolution land heterogeneity representation in understanding local impacts of future global scenarios with carbon markets and land competition influencing food prices. A methodologically unique Australian continental model is presented with bottom-up parcel scale granularity in land use change, food, carbon, water, and biodiversity ecosystem service supply determination, and partial equilibrium food price impacts of land competition. We show that food price feedbacks produce modest aggregate national land use and ecosystem service supply changes. However, high resolution results show amplified land use change and ecosystem service impact in some places and muted impacts in other areas relative to national averages. We conclude that fine granularity modelling of geographic diversity produces local land use change and ecosystem service impact insights not discernible with other approaches.