Land Management Conditions Research Articles

Estimating Afforestation Area Using Landsat Time Series and Photointerpreted Datasets

Afforestation processes, natural and anthropogenic, involve the conversion of other land uses to forest, and they represent one of the most important land use transformations, influencing numerous ecosystem services. Although remotely sensed data are commonly used to monitor forest disturbance, only a few reported studies have used these data to monitor afforestation. The objectives of this study were two fold: (1) to develop and illustrate a method that exploits the 1985–2019 Landsat time series for predicting afforestation areas at 30 m resolution at the national scale, and (2) to estimate afforestation areas statistically rigorously within Italian administrative regions and land elevation classes. We used a Landsat best-available-pixel time series (1985–2019) to calculate a set of temporal predictors that, together with the random forests prediction technique, facilitated construction of a map of afforested areas in Italy. Then, the map was used to guide selection of an estimation sample dataset which, after a complex photointerpretation phase, was used to estimate afforestation areas and associated confidence intervals. The classification approach achieved an accuracy of 87%. At the national level, the afforestation area between 1985 and 2019 covered 2.8 ± 0.2 million ha, corresponding to a potential C-sequestration of 200 million t. The administrative region with the largest afforested area was Sardinia, with 260,670 ± 58,522 ha, while the smallest area of 28,644 ± 12,114 ha was in Valle d’Aosta. Considering elevation classes of 200 m, the greatest afforestation area was between 400 and 600 m above sea level, where it was 549,497 ± 84,979 ha. Our results help to understand the afforestation process in Italy between 1985 and 2019 in relation to geographical location and altitude, and they could be the basis of further studies on the species composition of afforestation areas and land management conditions.

Analysis of Landscape Composition and Configuration Based on LULC Change Modeling

Land cover changes threaten biodiversity by impacting the natural habitats and require careful and continuous assessment. The standard approach for assessing these changes is land cover modeling. The present study investigated the spatio-temporal changes in Land Use Land Cover (LULC) in the Gorgan River Basin (GRB) during the 1990–2020 period and predicted the changes by 2040. First, a change analysis employing satellite imagery from 1990 to 2020 was carried out. Then, the Multi-Layer Perceptron (MLP) technique was used to predict the transition potential. The accuracy rate, training RMS, and testing RMS of the artificial neural network, MLP, and the transition potential modeling were computed in order to evaluate the results. Utilizing projections for 2020, the prediction of land cover change was made. By contrasting the anticipated land cover map of 2020 with the actual land cover map of 2020, the accuracy of the model was evaluated. The LULC conditions in the future were predicted under two scenarios of the current change trend (scenario 1) and the ecological capability of the land (scenario 2) by 2040. Seven landscape metrics were considered, including Number of Patches, Patch Density, the Largest Patch Index, Edge Density, Landscape Shape Index, Patch Area, and Area-Weighted Mean Shape Index. Based on the Cramer coefficient, the most critical factors affecting LULC change were elevation, distance from forest, and experimental probability of change. For the 1990–2020 period, the LULC change was shown to be influenced by deforestation, reduced rangeland, and expansion of agricultural and residential areas. Based on scenario 1, the area of forest, agriculture, and rangeland would face −0.8, 0.5, and 0.1% changes in the total area, respectively. In scenario 2, the area of forest, agriculture, and rangeland would change by 0.1, −1.3, and 1.3% of the total area, respectively. Landscape metrics results indicated the destructive trend of the landscape during the 1990–2020 period. For improving the natural condition of the GRB, it is suggested to prioritize different areas in need of regeneration due to inappropriate LULC changes and take preventive and protective measures where changes in LULC were predicted in the future, taking into account land management conditions (scenario 2).

Information support for land management in territories with special land management conditions

When managing land resources at various administrative and territorial levels, cadastral, monitoring and land management information is of great importance. Information from the real estate cadastre and the unified state register of real estate objects is the basis for creating a database on land resources and real estate objects, and predicting changes in their state. As far as the land management system is complex, the systematic approach to the management and protection of land resources is required; organizational and technological conditions should be linked with possible environmental and economic consequences within the boundaries of territories, including territories with a special land management regime. The boundaries of these territories do not coincide with the ones of settlements and are established on the basis of research materials used to determine the subject of a special land management regime. The article examines features of the system of information support for land management in territories with a special land management regime. The authors identified the main participants in the information space of land management, the composition of information and applied software solutions containing such information. A unified information system that integrates information for the purpose of coordinated management and coordination of functions should be created for territories with a special land management regime. Classification of some territories with special land management conditions as objects of the unified state register of real estate objects will improve the quality of land management decisions. The studies conducted on the example of the Ples cluster showed that the share of cadastral cost in the total land management cost is insignificant and entails a greater number of benefits (economic, environmental, informational, etc.).

Site-specific machine learning predictive fertilization models for potato crops in Eastern Canada.

Statistical modeling is commonly used to relate the performance of potato (Solanum tuberosum L.) to fertilizer requirements. Prescribing optimal nutrient doses is challenging because of the involvement of many variables including weather, soils, land management, genotypes, and severity of pests and diseases. Where sufficient data are available, machine learning algorithms can be used to predict crop performance. The objective of this study was to determine an optimal model predicting nitrogen, phosphorus and potassium requirements for high tuber yield and quality (size and specific gravity) as impacted by weather, soils and land management variables. We exploited a data set of 273 field experiments conducted from 1979 to 2017 in Quebec (Canada). We developed, evaluated and compared predictions from a hierarchical Mitscherlich model, k-nearest neighbors, random forest, neural networks and Gaussian processes. Machine learning models returned R2 values of 0.49-0.59 for tuber marketable yield prediction, which were higher than the Mitscherlich model R2 (0.37). The models were more likely to predict medium-size tubers (R2 = 0.60-0.69) and tuber specific gravity (R2 = 0.58-0.67) than large-size tubers (R2 = 0.55-0.64) and marketable yield. Response surfaces from the Mitscherlich model, neural networks and Gaussian processes returned smooth responses that agreed more with actual evidence than discontinuous curves derived from k-nearest neighbors and random forest models. When conditioned to obtain optimal dosages from dose-response surfaces given constant weather, soil and land management conditions, some disagreements occurred between models. Due to their built-in ability to develop recommendations within a probabilistic risk-assessment framework, Gaussian processes stood out as the most promising algorithm to support decisions that minimize economic or agronomic risks.

Development of a new method for estimating SCS curve number using TOPMODEL concept of wetness index (case study: Kasilian and Jong watersheds, Iran)

In recent years, several empirical and mathematical methods have been developed to estimate runoff, among which the SCS curve number (SCS-CN) method is one of the simplest and most widely used methods. The SCS-CN depends mainly on a CN parameter which corresponds to various soil, land cover, and land management conditions, selected from look-up tables. An application of GIS and RS techniques along with filed investigations made it possible to enhance the method from a lumped one to the level of semi-distributed models in which a specific value can be assigned to each cell in raster maps. The up-to-date procedures require several datasets, field measurements and overlying issues which limits the use of SCS-CN in data-scarce regions. In this research a new method has been developed which estimates the SCS-CN over the catchment with a minimum input dataset and acceptable accuracy and is based on the saturation-excess concept, which is used in the semi-distributed model: TOPMODEL. The proposed method depends on three parameters, including ndrain (soil porosity), $${\bar{\text{z}}}$$ (average distance to watershed water table surface) and m (which controls the effective depth of the saturated soil) and one input dataset, the so-called topographic index. Results showed that the maximum and minimum differences between the basin-averaged CN based on the GIS and RS techniques and the proposed method for Kasilian and Jong watersheds are 12% and 0.3%, respectively. Also, the findings indicated that, of the three parameters of proposed method, the m parameter plays a key role and that by increasing this parameter the basin-averaged CN tends to decrease and vice versa. Because of the dependence on a topographic index, the proposed method is strongly affected by DEM resolution and there are significant differences between low and high-resolution DEMs. However, for a small scale watershed, similar to Kasilian, using DEMs with resolution lower than 100 m considerably decreases the above differences. As an overall conclusion, the proposed method provides acceptable values of SCS-CN which is important for running rainfall-runoff model in a data-limited or data-scarce regions. In addition, creating the gridded map for CN, which is required in most hydrological models, is one of the most important advantages of the proposed method.

Development of PLEAD: A Database Containing Event-based Runoff Phosphorus Loadings from Agricultural Fields.

Computer models are commonly used for predicting risks of runoff P loss from agricultural fields by enabling simulation of various management practices and climatic scenarios. For P loss models to be useful tools, however, they must accurately predict P loss for a wide range of climatic, physiographic, and land management conditions. A complicating factor in developing and evaluating P loss models is the relative scarcity of available measured field data that adequately capture P losses before and after implementing management practices in a variety of physiographic settings. Here, we describe the development of the P Loss in runoff Events from Agricultural fields Database (PLEAD)-a compilation of event-based, field-scale dissolved and/or total P loss runoff loadings from agricultural fields collected at various research sites located in the US Heartland and southern United States. The database also includes runoff and erosion rates; soil-test P; tillage practices; planting and harvesting rates and practices; fertilizer application rate, method, and timing; manure application rate, method, and timing; and livestock grazing density and timing. In total, >1800 individual runoff events-ranging in duration from 0.4 to 97 h-have been included in the database. Event runoff P losses ranged from <0.05 to 1.3 and 3.0 kg P ha for dissolved and total P, respectively. The data contained in this database have been used in multiple research studies to address important modeling questions relevant to P management planning. We provide these data to encourage additional studies by other researchers. The PLEAD database is available at .

Evaluating the APEX Model for Simulating Streamflow and Water Quality on Ten Agricultural Watersheds in the U.S.

To better understand the Agricultural Policy Environmental Extender (APEX) model’s strengths and limitations in simulating streamflow and water quality, we evaluated its performance under a diverse range of climatic, topographic, soil, cover, and land management conditions using three parameter settings: best professional judgment (BPJ), partially calibrated, and fully calibrated. A total of 18 calibration parameters governing streamflow, crop yield, and sediment, N, and P were adjusted. Hydrologic and water quality responses were simulated on ten USDA-ARS watersheds with heterogeneous forested, pasture/range, and/or corn-soybean cropping systems. Based on percent bias and Nash-Sutcliffe coefficient of efficiency test statistics, 0%, 0%, 15%, and 9% of the data sets were considered satisfactory or better in simulating monthly streamflow, sediment, N, and P, respectively, in the BPJ mode. However, when used with the fully calibrated settings, 73%, 43%, 38%, and 27% of the data sets were satisfactory or better. Findings indicate that when fully calibrated, APEX estimates streamflow very well, sediment and nitrogen moderately well, and phosphorus marginally well at a monthly time scale. However, when using the BPJ approach, APEX does not estimate watershed-level streamflow, sediment, or nutrients accurately at a monthly time step. Additionally, APEX lacks an element of robustness in simulating streamflow and water quality constituents when applied to the same watershed with a different period of record or to a nearby watershed with a similar period of record. Based on these findings, it is recommended that users exercise caution when employing APEX with the BPJ approach or in validation mode at watershed scales.

Assessing the effectiveness of sustainable land management policies for combating desertification: A data mining approach

This study investigates the relationship between fine resolution, local-scale biophysical and socioeconomic contexts within which land degradation occurs, and the human responses to it. The research draws on experimental data collected under different territorial and socioeconomic conditions at 586 field sites in five Mediterranean countries (Spain, Greece, Turkey, Tunisia and Morocco). We assess the level of desertification risk under various land management practices (terracing, grazing control, prevention of wildland fires, soil erosion control measures, soil water conservation measures, sustainable farming practices, land protection measures and financial subsidies) taken as possible responses to land degradation. A data mining approach, incorporating principal component analysis, non-parametric correlations, multiple regression and canonical analysis, was developed to identify the spatial relationship between land management conditions, the socioeconomic and environmental context (described using 40 biophysical and socioeconomic indicators) and desertification risk. Our analysis identified a number of distinct relationships between the level of desertification experienced and the underlying socioeconomic context, suggesting that the effectiveness of responses to land degradation is strictly dependent on the local biophysical and socioeconomic context. Assessing the latent relationship between land management practices and the biophysical/socioeconomic attributes characterizing areas exposed to different levels of desertification risk proved to be an indirect measure of the effectiveness of field actions contrasting land degradation.

Humanitarian Demining and Sustainable Land Management in Post-Conflict Settings in Sri Lanka: Literature Review

&lt;p&gt;Systematic humanitarian demining carried out with care is an essential prerequisite for sustainable land management in post conflict settings. Degradation of land and pollution of water, soil and vegetation, as well as poisonous gas emissions that may even contribute to climate change, can be reduced significantly by humanitarian demining practices. Such practices simultaneously conserve natural resources and increase yields which results to sustainable land management. Mine Risk Education which is a major component of humanitarian demining, will have a lasting impact on people’s knowledge, attitudes and practices related to landmines making a positive contribution towards sustainable land management. This paper utilizes research publications from refereed journals and mine action authorities as well as ground information using the systematic literature review (SLR) method. The study investigates relations between humanitarian demining and sustainable land management in post conflict settings with a classic example from North East Sri Lanka. The practical implications for demining operators are that they can implement the strategies to improve the prevailing sustainable land management conditions of the communities in Sri Lanka and elsewhere.&lt;/p&gt;

Trade-offs between ecosystem services and alternative pathways toward sustainability in a tropical dry forest region

The design of strategies aimed at sustainable resource management requires an understanding of the trade-offs between the ecosystem services at stake, to determine appropriate ways in which to navigate them. We assess trade-offs between forage production for cattle ranching and the maintenance of carbon stocks or tree diversity in a Mexican tropical dry forest. Trade-offs between pairs of services were assessed by identifying their efficiency frontiers at both site and landscape scales. We also estimated service outcomes under current and hypothetical land-management conditions. We found stark trade-offs between fodder and carbon stocks and between fodder and tree species richness at the site scale. At the landscape scale, the efficiency frontier was concave, with a much less pronounced trade-off in the fodder-species richness case. Our estimates of current service supply levels showed a reduction of 18-21% for C stock and 41-43% for fodder biomass, relative to the maximum feasible values along the efficiency frontier. Choice of the optimum management strategy to reduce such inefficiency depended on deforestation level: secondary forest regeneration was most suitable when deforestation is low, whereas increased fodder productivity in the pastures is best when deforestation is high. Pasture enrichment with forage trees and secondary forest growth are potential management alternatives for achieving sustainability given the range of enabling ecological factors and to balance ecological and social sustainability given the requirements and preferences of local stakeholders. Given that analogous trade-offs are found across the tropics, this work contributes to reconciling tropical forest maintenance and its use for sustainable rural livelihoods.

Application of remote sensing and geographical information system for generation of runoff curve number

Watershed is an ideal unit for planning and management of land and water resources (Gajbhiye et al., IEEE international conference on advances in technology and engineering (ICATE), Bombay, vol 1, issue 9, pp 23–25, 2013a; Gajbhiye et al., Appl Water Sci 4(1):51–61, 2014a; Gajbhiye et al., J Geol Soc India (SCI-IF 0.596) 84(2):192–196, 2014b). This study aims to generate the curve number, using remote sensing and geographical information system (GIS) and the effect of slope on curve number values. The study was carried out in Kanhaiya Nala watershed located in Satna district of Madhya Pradesh. Soil map, Land Use/Land cover and slope map were generated in GIS Environment. The CN parameter values corresponding to various soil, land cover, and land management conditions were selected from Natural Resource Conservation Service (NRCS) standard table. Curve number (CN) is an index developed by the NRCS, to represent the potential for storm water runoff within a drainage area. The CN for a drainage basin is estimated using a combination of land use, soil, and antecedent soil moisture condition (AMC). In present study effect of slope on CN values were determined. The result showed that the CN unadjusted value are higher in comparison to CN adjusted with slope. Remote sensing and GIS is very reliable technique for the preparation of most of the input data required by the SCS curve number model.

Advances in the prognosis of soil sodicity under dryland irrigated conditions

Salt-affected soils, both saline and sodic, may develop under both dryland and irrigated conditions, affecting the physical and chemical soil properties, with negative consequences in the environment, in crop production and in animal and human health. Among the development processes of salt-affected soils, the processes of sodification have generally received less attention and are less understood than the development of saline soils. Although in both, hydrological processes are involved in their development, in the case of sodic soils we have to consider some additional chemical and physicochemical reactions, making more difficult their modeling and prediction. This is especially true where we have to consider the effects of the groundwater level and composition. In this contribution there are presented three case studies: one related to the development of sodic soils in the lowlands of the Argentina Pampas, under dry-land conditions with sub-humid temperate climate and pastures for cattle production; the second deals with the development of sodic soils in the Colombia Cauca Valley, under irrigated conditions and tropical sub-humid climate, in lands used for sugarcane cropping dedicated to sugar and ethanol production; and the last one related to the sodification of soils in the Western Plains of Venezuela, under irrigated conditions, sub-humid tropical climate and continuous cropping of rice under flooding. The development of sodicity in the surface soil is partially related to the composition and level of the ground-water, mainly affected in the Argentina case by drainage conditions, in the case of Colombia to the inefficient irrigation and inadequate drainage, and in the case of Venezuela to the soil management and irrigation system. There is shown how the model SALSODIMAR, developed by the author, based on the balance of water and soluble components of both irrigation water and ground-water, under different water and land management conditions, may be successfully adapted for the diagnosis and prediction of the different processes and problems, and for selection of alternatives for their prevention and amelioration.

Finding options to improve catchment water quality—Lessons learned from historical land use situations in a Mediterranean catchment in Slovenia

The main objective of this study is to investigate the impact of the historical land use situations on the river water quality under present climate and land management conditions and what can be lessons learned from it. The historical land use situations are based on digitised historical maps of existing past land use cover distribution of small Slovenian catchment (River Reka) from 1787, 1827, 1940 and 1984. Maps were compared between each other and baseline land use situation in 2009. The well-known river basin model SWAT was used to simulate the influences of land use situations on water quality, especially in terms of suspended sediments concentration. Results indicate that the historical land use situations would decrease water quality. Lessons learned from the study are as follows: (1) based on the more than 200-year-wide research time window, we can state that vineyards and orchards are preferred agricultural land use with undesired side effect on water quality; (2) climate, terrain characteristics and wine demand on the market are the driving factors for the land use pattern – share of vineyards remains fairly constant through the years, regardless of changes in authorities; (3) historical land use patterns would, in present times, cause an increased occurrence of erosion and deterioration of environmental conditions for organisms in surface waters; and (4) the present land use pattern with regard to the observed sediment concentration is still not an optimal solution. Further land use adjustments or agri-environmental measures are required to achieve water quality improvements. Historical examples can serve as lessons learned for the future with the purpose of minimising the influences of planned land use changes on water resource quality and quantity.

A comparison of rainfall-runoff modelling approaches for estimating impacts of rural land management on flood flows

There is a requirement for predictive tools to assist in land management and flood risk planning, and a variety of tools have been proposed recently. We compare four tools developed under various UK research programmes. The strengths and limitations of the tools are reviewed, model performances on historic data are assessed, and the methods are applied to estimating flood flows of 5- and 10-year return periods, and flow peaks under both recent land management conditions and speculative scenarios (grazing intensification and tree planting), using the Pontbren catchment, UK as a case study. Overall, the models agree on the direction of change, so that heavy grazing increases, and afforestation and tree strips decrease the flood flows. However, the estimated effects vary significantly between methods. It is concluded that method selection needs to carefully consider the type and scale of land management scenario being examined, and the sources of data available to support the modelling. Using an ensemble of suitable models is proposed as a useful way to represent a multi-expert opinion and to characterise the structural error associated with a single model.

SCS-CN parameter determination using rainfall-runoff data in heterogeneous watersheds – the two-CN system approach

Abstract. The Soil Conservation Service Curve Number (SCS-CN) approach is widely used as a simple method for predicting direct runoff volume for a given rainfall event. The CN parameter values corresponding to various soil, land cover, and land management conditions can be selected from tables, but it is preferable to estimate the CN value from measured rainfall-runoff data if available. However, previous researchers indicated that the CN values calculated from measured rainfall-runoff data vary systematically with the rainfall depth. Hence, they suggested the determination of a single asymptotic CN value observed for very high rainfall depths to characterize the watersheds' runoff response. In this paper, the hypothesis that the observed correlation between the calculated CN value and the rainfall depth in a watershed reflects the effect of soils and land cover spatial variability on its hydrologic response is being tested. Based on this hypothesis, the simplified concept of a two-CN heterogeneous system is introduced to model the observed CN-rainfall variation by reducing the CN spatial variability into two classes. The behaviour of the CN-rainfall function produced by the simplified two-CN system is approached theoretically, it is analysed systematically, and it is found to be similar to the variation observed in natural watersheds. Synthetic data tests, natural watersheds examples, and detailed study of two natural experimental watersheds with known spatial heterogeneity characteristics were used to evaluate the method. The results indicate that the determination of CN values from rainfall runoff data using the proposed two-CN system approach provides reasonable accuracy and it over performs the previous methods based on the determination of a single asymptotic CN value. Although the suggested method increases the number of unknown parameters to three (instead of one), a clear physical reasoning for them is presented.

Estimating the influence of land management change on weed invasion potential using expert knowledge

AbstractAim To develop and test a general framework for estimating weed invasion potential (suitability and susceptibility) that utilized expert knowledge of dispersal, establishment and persistence and considered the influence of land management.Location The semi‐arid Desert Channels Region of Queensland, Australia (476,000 km2).Methods We developed a general framework that integrated knowledge and empirical data of the environmental and land management variables influencing the dispersal, establishment and persistence of the invasive shrub parkinsonia (Parkinsonia aculeata) using a Bayesian network linked to a Geographic Information System (GIS). We evaluated the influence of different land management scenarios on landscape suitability for parkinsonia. Model performance was assessed by comparing predicted landscape suitability with mapped parkinsonia locations and estimated parkinsonia density.Results Our predictions of moderate to high suitability corresponded reasonably well with mapped parkinsonia locations (71% match) and areas of common to abundant estimated density (92% match). They also suggested that parkinsonia has not reached its potential distribution within the study region. Under current land management conditions, 77,000 km2 of land was found to be highly or moderately suitable for parkinsonia. Scenario analysis indicated that maintaining moderate herbaceous ground cover levels, and using sheep to browse juvenile parkinsonia, reduced the predicted moderate to high suitability area to 27,000 km2, offering a potential management strategy for limiting parkinsonia invasion.Main conclusions Weed invasion potential can be reasonably estimated using expert knowledge of dispersal, establishment and persistence, integrated using a Bayesian network linked to a GIS. This modelling approach can be an alternative to process‐based and phenomenological modelling, which can be problematic for modelling new and emerging weed invasions, particularly where data are patchy. The modelling approach also allows the influence of land management change on invasion potential to be investigated through scenario analysis.

Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions

Decision-making processes in agriculture often require reliable crop response models to assess the impact of specific land management. While process-based models are often preferred over empirical ones in current modelling communities, empirical crop growth models can play an important role in identifying the hidden structure of crop growth processes relating to a wide range of land management options. This study investigates the potential of predicting crop yield responses under varying soil and land management conditions by applying three different adaptive techniques: general linear models (GLMs), artificial neural networks (ANNs), and regression trees (RTs). The crop yield data used in this research consist of 720 maize yield indices from 11 different land management trials in southern Uganda. GLM showed the poorest results in terms of modelling accuracy, prediction accuracy, and model uncertainty, which might suggest its inability to model the non-linear causal relationships present in complex soil–land and crop-management interactions. The other two non-parametric adaptive models show significantly higher prediction accuracy than GLM. RT is the most robust technique for predicting crop yield at the study site. ANN is also a promising tool for predicting crop yield and offers insight into the causal relationships through the use of sensitivity analyses, but the complex parameterization and optimum model structure require further attention. The three adaptive techniques compared in this research showed different advantages and disadvantages. When these methods are used together, valuable information can be provided on crop responses, and more reliable crop growth models may result.

Soil enzyme activities and N2O emissions under different land management conditions.

Land Management Conditions H. Jian-gang, L. Zhan-bin, Z. Yong-li, B. Hong-ying, Q. Dong 1 College of Resources and Environment Science, Northwestern Sci-tech University of Agriculture and Forestry, 22 Xi’nong Road, Yangling 712100, Shaanxi, People’s Republic of China 2 State Key Laboratory of Soil Erosion and Dry Land Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, 26 Xi’nong Road, Yangling 712100, Shaanxi, People’s Republic of China 3 Xi’an University of Technology, Xian 710048, Shaanxi, People’s Republic of China 4 The Institute of Subtropical Agric-ecology of Chinese Academy of Sciences, Changsha 410125, Hu’nan, People’s Republic of China

The Forest Fires in Indonesia 1997-98: Possible Causes and Pervasive Consequences

The recent (September 1997 to June 1998) forest fires in Indonesia represent an unprecedented ecological disaster. The precondition for rampant burning was a prolonged drought triggered by the El Niño, but prevailing land use and land management conditions were such that a disaster of this kind was inevitable. The humid-tropical nation has little experience of dry weather, and people adhere to deeply-ingrained fire-using habits. Fire has long been used as a cheap means for land clearance by farmers and by plantation and forestry-concession owners. Hundreds of hotspots flared up within a short period, with marked clustering in Kalimantan and Sumatra. Some 1.5 million ha have been burnt, including valuable protected areas. The remoteness of the locations and lack of resources, organisation and expertise combined to make fire control impossible, and the smoke soon spread to neighbouring countries, bringing health problems and heavy economic losses. Ecological damage includes disruption of ecosystem functions and habitats, the decimation of plant and animal life, including endangered species, and irreparable losses in biodiversity. Given the institutional obstacles and recent economic decline in Indonesia, the prognosis is not an optimistic one.

Nitrous oxide emissions under different soil and land management conditions

Nitrous oxide (N2O) emissions of three different soils – a rendzina on cryoturbed soil, a hydromorphic leached brown soil and a superficial soil on a calcareous plateau – were measured using the chamber method. Each site included four types of land management: bare soil, seeded unfertilized soil, a suboptimally fertilized rapeseed crop and an overfertilized rapeseed crop. Fluxes varied from –1g to 100g N2O-nitrogen ha–1 day–1. The highest rates of N2O emissions were measured during spring on the hydromorphic leached brown soil which had been fertilized with nitrogen (N); the total emissions during a 5-month period exceeded 3500gNha–1. Significant fluxes were also observed during the summer. Very marked effects of soil type and management were observed. Two factors – the soil hydraulic behaviour and the ability of the microbial population to reduce N2O – appear to be essential in determining emissions of N2O by soils. In fact, the hydromorphic leached brown soil showed the highest emissions, despite having the lowest denitrification potential because of its water-filled pore space and low N2O reductase activity. Soil management also appears to affect both soil nitrate content and N2O emissions.