Agricultural Land Management Practices Research Articles

Projecting the response of carbon sink potential to land use/land cover change in ecologically fragile regions

Introduction: The carbon storage service of ecosystems in ecologically fragile areas is highly sensitive to regional land use/land cover (LULC) changes. Predicting changes in regional carbon storage under different LULC scenarios is crucial for land use management decisions and exploring carbon sink potential. This study focuses on the Luan River Basin, a typical ecologically fragile area, to analyze the impact of LULC changes on carbon storage.Methods: The PLUS-InVEST model was employed to simulate LULC patterns for the year 2030 under three scenarios: natural development, cropland protection and urban development, and ecological protection. The model projected the future carbon sink potential of the basin under these scenarios.Results: From 2000 to 2020, carbon storage showed a trend of decrease followed by an increase. By 2030, compared to 2020, carbon storage is projected to increase by 16.97% under the ecological protection scenario and decrease by 22.14% under the cropland protection and urban development scenario. The increase in carbon storage was primarily due to the conversion of cropland and grassland to forestland, while the decrease was mainly associated with the conversion of forestland to grassland and cropland, and the transformation of grassland to cropland and construction land. In the potential LULC scenarios of 2030, certain regions within the basin exhibited unstable carbon sink potential, strongly influenced by LULC changes. These areas were predominantly characterized by artificially cultivated forests, shrubs, and agricultural land. Implementing appropriate forest management measures and optimizing agricultural land management practices are essential to enhance carbon sink potential in these regions. Population density, annual average temperature, and DEM (Digital Elevation Model) were the dominant factors driving the spatial variation of carbon sink potential in the Luan River Basin.Discussion: The research results provide a theoretical basis for rational planning of land use and the enhancement of carbon sink potential in ecologically fragile regions.

Crop cover and nutrient levels mediate the effects of land management type on aquatic invertebrate richness in prairie potholes.

Aquatic invertebrates provide important ecosystem services, including decomposition and nutrient cycling, and provide nutrition for birds, fish, amphibians, and bats. Thus, the effects of agricultural land management practices on aquatic invertebrates are relevant to farmers, wildlife biologists, and policymakers. Here, we used data on aquatic invertebrates (159 taxa, 73 to species, 75 to genus/family) collected in 40 wetlands in the Canadian prairies to test for direct and indirect relationships among land management types (perennial cover, organic, minimum tillage, conventional), landscape structure (cropland and wetland cover within the surrounding landscape), and water quality (total nutrient levels, turbidity) on species richness of invertebrates using structural equation modelling. Additionally, we assessed variation in community composition within and among wetlands in different land use management types using a direct gradient analysis and variance partitioning. The direct effects of land management type were not supported but we found strong supportive evidence that effects of land management on richness were significantly mediated through cropland cover, nutrient levels, and turbidity. After controlling for these indirect effects, aquatic invertebrate richness decreased along a gradient from the lowest to the highest farming intensity, i.e., richness decreased from perennial cover sites to organic to minimum tillage to conventional sites. Support was also found for negative effects of nutrient levels and turbidity on richness. We did not find significant support for differences in gamma diversity or a simple test (homogeneity of multivariate dispersions) of differences in turnover among land management types; however, land management had a significant effect in distance-based redundancy analysis. Taken together, these results suggest that focusing conservation efforts on reducing cropland erosion and nutrient inputs to wetlands and creating more permanent cover may be effective strategies for conserving richness of aquatic invertebrates in agricultural landscapes in this region.

Contemporary Agricultural Land Management Practices under Climate Change and Population Increase Scenarios: Insights from Tanzania’s Southern Highlands

This study investigated the impact of population growth and climate change on land management practices. The study was conducted in Mbinga district located in the Southern Highlands of Tanzania. Quantitative and qualitative research approaches were employed. Research data were collected using structured questionnaires, in-depth interviews, physical observation, focus group discussions and literature review. A total of 267 household heads were selected for this study. Also, the study used climate data from Tanzania Meteorological Authority, temperature and rainfall data over the past 40 years were collected (1981-2021). The findings revealed that, the area has experienced increased population and climate change incidences. The trend line for both minimum and maximum temperatures indicate a substantial increased trends at (P= 0.0018, R2=0.0005; P=0.0176, R2=0.02359) respectively. Contrariwise, the area has experienced decrease in rainfall totals at (P= 0.4385, R2=0.0008). It was revealed that, various traditional methods of land management practices including the use of animal manures, mulching, fallowing and the use of crop residues were affected by population increase and climate change. Farmers adopted new agricultural practices including the use of industrial fertilizers and reducing the size of contour terraces as the coping strategies for agricultural production against environmental deterioration caused by population increase and climate change. However, the sustainability of such practices is uncertain due to continued population increase and climate change scenarios. Therefore, the study recommends for introduction of sustainable land management practices that would instigate for sustainable food production for the changing population.

Effects of agricultural land management practices on crop production and household income in Ojoje, southern Ethiopia

Farmers in Ethiopia and other sub-Saharan African countries often implement various agricultural land management (ALM) practices to mitigate the negative impact of land degradation, increase crop yield, improve income, and safeguard the security of food. Despite the widespread use of these measures, their effect on crop productivity and related income has received relatively little attention in research, particularly in the context of mixed farming systems. The objective of this study was to assess the effects of ALM practices on crop production and household income in southern Ethiopia. Data was gathered from 423 sampled respondents, which included adopters and non-adopters of ALM practices. This study adopted multistage sampling to gather the data through a survey questionnaire. This study’s findings reveal that ALM strategies have a considerable impact on crop productivity and the incomes of farm households (p<0.05). Moreover, the adoption of integrated physical and biological intervention practices for longer durations resulted in a steady increase in both crop productivity and household incomes. Furthermore, analysis of the effect of long-term treatment indicates that farms show a significant and increasing marginal benefit to production after six years of intervention. These findings imply that integrating ALM practices and maintaining them for an extended period (i.e., 10 years) will help to increase crop productivity and household incomes. Hence, adopting a wider range of physical and biological ALM measures and ensuring their continual adoption are key strategies for improving crop production and increasing household income. This strategy will have significant policy implications and provide a solid foundation for sustainable agricultural development.

Predicting the spatial variation in cost-efficiency for agricultural greenhouse gas mitigation programs in the U.S.

BackgroundTwo major factors that determine the efficiency of programs designed to mitigate greenhouse gases by encouraging voluntary changes in U.S. agricultural land management are the effect of land use changes on producers’ profitability and the net sequestration those changes create. In this work, we investigate how the interaction of these factors produces spatial heterogeneity in the cost-efficiency of voluntary programs incentivizing tillage reduction and cover-cropping practices. We map county-level predicted rates of adoption for each practice with the greenhouse gas mitigation or carbon sequestration benefits expected from their use. Then, we use these bivariate maps to describe how the cost efficiency of agricultural mitigation efforts is likely to vary spatially in the United States.ResultsOur results suggest the combination of high adoption rates and large reductions in net emissions make reduced tillage programs most cost efficient in the Chesapeake Bay watershed or the Upper Mississippi and Lower Missouri sub-basins of the Mississippi River. For programs aiming to reduce net emissions by incentivizing cover-cropping, we expect cost-efficiency to be greatest in the areas near the main stem of the Mississippi River within its Middle and Lower sections.ConclusionsMany voluntary agricultural conservation programs offer the same incentives across the United States. Yet spatial variation in profitability and efficacy of conservation practices suggest that these uniform approaches are not cost-effective. Spatial targeting of voluntary agricultural conservation programs has the potential to increase the cost-efficiency of these programs due to regional heterogeneity in the profitability and greenhouse gas mitigation benefits of agricultural land management practices across the continental United States. We illustrate how predicted rates of adoption and greenhouse gas sequestration might be used to target regions where efforts to incentivize cover-cropping and reductions in tillage are most likely to be cost -effective.

Survey of Missouri Landowners to Explore the Potential of Woody Perennials to Integrate Conservation and Production

The state of Missouri, USA offers a unique opportunity for tree planting under several federal conservation programs. However, many landowners remain hesitant to enroll and take land out of agricultural production. This study explores the willingness of landowners to adopt agroforestry systems with food producing tree and shrub species through federal conservation program funding using mail and online surveys. Surveys followed the Dillman Tailored Design Method to collect data on landowners’ farm characteristics, production practices, and land management choices. Survey participants were sampled on a county basis within each of the six major geographic regions of the state. Twelve counties were randomly selected, and surveys were mailed to a proportional sampling of farm addresses gathered from each of the county tax assessor offices. The goal of the survey was to (1) identify landowners’ current land management practices and goals, (2) understand landowners’ perceptions of and preferences for different planting plans for their farm, and (3) capture landowners’ interest in participating in conservation programs to assist in the planting of trees and shrubs on their land. Our analysis of this survey found that landowners are receptive to agroforestry plantings, rating them higher on average than traditional agricultural land management practices. Landowner age, past participation in a conservation program, and presence of marginal land all had significant correlation with willingness to adopt agroforestry. The inclusion of technical assistance or federal conservation funding was found to increase the willingness of landowners to plant multifunctional agroforestry designs.

Sampling frequency, load estimation and the disproportionate effect of storms on solute mass flux in rivers

Riverine discharge (Q) and dissolved concentrations (C) dictate solute mass export from watersheds. Commonly Q is tracked at a much higher frequency than C for most major solutes, leading to the necessity of load estimation algorithms which are often based on sparse data. The result is that the disproportionate effects of short-duration events (e.g., storms) on solute mass fluxes are poorly known. Here we use novel lab-in-the-field instrumentation to compare high temporal-resolution (∼30 min to 7 h) datasets of major ion chemistry collected over a year of continuous monitoring in three watersheds ranging over four orders of magnitude in drainage area. In these diverse settings, we quantify the errors associated with common load estimation algorithms and reduced sampling frequencies. When sample frequencies are coarsened, the mass flux of solutes which are diluted by storm events (i.e., Ca2+, Mg2+, Na+, Cl− and SO42−) are systematically overestimated, while nutrients which become mobilized by these events (K+ and NO3−) are underestimated. This is most pronounced in the largest river, and strongly tied to the increasing likelihood that storm events are missed as sampling frequencies decrease. Coarsening our high-resolution data to monthly sampling frequency yields an average overestimate of 8 % for Na+ and an average underestimate of 32.5 % for K+ across the three watersheds, illustrating clear implications for estuary and coastal water eutrophication, chemical weathering budgets, and agricultural land management practices. SynopsisA new ‘lab-in-the-field’ technology produces continuous high-frequency records of the full suite of major ions in rivers. These data highlight the disproportionate effect of large storms on catchment solute exports and the error associated with temporally coarse monitoring.

Risk and ambiguity aversion: Incentives or disincentives for adoption of improved agricultural land management practices?

AbstractFarmers grapple with uncertainties related to the adoption of improved agricultural practices. Adoption decisions on these practices may vary in response to risk in which farmers may be able to predict the chances of occurrence on outcomes of their decisions, and/or ambiguity where adoption‐decision outcome probabilities are unknown. Also, these uncertainties may emanate from imperfect information on production returns from adoption and adoption‐related fixed costs. In this paper, we study whether risk and ambiguity aversion create incentives or disincentives for intensity and duration of adoption of improved agricultural practices. For this, we combine experimental with survey data. We find that ambiguity aversion reduces incentives for high‐intensity adoption while risk aversion attenuates this disincentive. As expected, risk aversion increases incentives for longer adoption of improved agricultural practices. Experience was expected to allow farmers to learn enough about payoff probabilities from adoption and render ambiguity aversion less important. Yet, we find it reduces the duration of adoption of soil and water conservation structures, which could be explained by adoption‐related high fixed costs. On the other hand, ambiguity aversion appears to matter little with adoption of agroforestry despite the associated high fixed and management costs attributed to integrated agri‐silviculture and agro‐silvopastoral systems. Given the complementary nature of the practices, agricultural policy should be directed at interventions that attenuate the deterrent effect of ambiguity aversion through providing information related to production returns from the adoption of improved practices that involve high fixed (management) costs.

What is the role of biodiversity in mediating the effects of climate change on New Zealand’s future agroecosystems?

ABSTRACT Climate change will have far-reaching negative impacts on all aspects of Earth’s state and functions, including ongoing biodiversity decline and threats to agricultural production. These effects will be dependent on geographic location; for example, parts of New Zealand are predicted to have increased flooding, drought and wildfires, depending on the local environmental context. Effects of climate change on agricultural production will be both direct, such as crop losses due to flooding, and indirect, such as increased invasive pest insect and weed pressure on horticultural production or decline in water capture capacity in pastoral South Island High Country tussock grasslands due to increased fire frequency combined with grazing. It is crucial to understand the complex, interactive effects of climate change on agroecosystems, mediated by biodiversity, if human interventions, such as land management, are to be developed and effectively applied to mitigate negative consequences. Even better is if those interventions can be used to address the biodiversity crisis. Nature-based Solutions is a framework that offers such solutions; however, improved scientific understanding of these interacting processes within agroecosystems is required at multiple temporal and spatial scales to justify sector investment for changes in agricultural land management practices that enhance production and native biodiversity.

Soil organic carbon, aggregation and fungi community after 44years of no-till and cropping systems in the Central Great Plains, USA.

Implementing sustainable agricultural land management practices such as no-till (NT) and diversified crops are important for maintaining soil health properties. This study focuses on the soil health of three long-term (44years) tillage systems, NT, reduced tillage (RT), and conventional tillage (CT), in monoculture winter wheat-fallow (W-F) (Triticum aestivum L.) and wheat-soybean (W-S) (Glycine max (L.) Merrill) rotation. Soil organic carbon (C) was higher in NT than CT in the surface 0-5cm, but not different in the 5-15cm, demonstrating SOC stratification on the soil profile. The soil water content was higher in NT followed by RT and CT in the top 0-5cm. We found an association between increased carbon, aggregation, and AMF biomass. Greater soil aggregation, carbon and AMF were observed in NT at 0-5cm soil depth. The W-S cropping system had greater soil microbial community composition based on fungi biomass, AMF and fungal to bacteria ratio from phospholipid fatty acid analysis (PLFA). Large macroaggregates were positively correlated with total C and N, microbial biomass, Gram + , and AMF. Soil water content was positively correlated with macroaggregates, total C and N, and AC. No-till increased soil carbon content even after 44years of cultivation. By implementing conservation tillage systems and diversified crop rotation, soil quality can be improved through greater soil organic C, water content, greater soil structure, and higher AMF biomass than CT practice in the Central Great Plains.

Effect of agricultural land management practices on the selected soil quality indictors: empirical evidences from the south Ethiopian highlands

BackgroundLand degradation is a major challenge that adversely affects soil fertility, agricultural production, and environmental sustainability. To curb this, various agricultural land management (ALM) measures have been practiced for the last three decades. This research investigated the effects of ALM practices on selected soil quality indicators in the Ojoje sub-watershed, Southern Ethiopia Highlands. A total of 72 composite soil samples were collected from non-treated and treated plots (i.e., land treated for 5 and 10 years with only physical practices and integrated measures) at a depth of 0–20 cm. A one-way ANOVA was used to demonstrate statistically significant variations on soil quality indicators. Simple regression analysis was used to explain the proportional variance of soil quality indicators due to ALM measures.ResultThe findings of the study indicate that integrated ALM practices have positive effects on the soil quality indicators. Most soil quality indicators, such as the soil organic carbon, soil organic matter, total nitrogen, available phosphorous, sulfur, boron and percentage of cation exchange capacity, were significant (p < 0.01 and p < 0.05) as a result of ALM practices. However, soil bulk density, potassium and percentage of silt contents were higher, but the difference was insignificant. Thus, the mean value of soil quality indicators increased steadily with age of intervention and application of integrated physical and biological conservation measures.ConclussionALM practices had stronger effects when land was treated with integrated ALM measures and conserved for an extended period of time. Hence, integrating ALM practices and maintaining them for the long term is crucial for improving soil quality and enhancing agricultural productivity.

A multi-millennial reconstruction of gully erosion in two contrasting Mediterranean catchments

Cereal and olive cultivation are thought to have played a major role in triggering soil erosion since humans have settled in the south of Spain and in increasing gully activity since the relatively recent intensification of olive cultivation. To test these hypotheses, we dated the sediments and reconstructed the soil erosion history of two gullied catchments, in Baena and Montefrío, both located in the South of Spain but with well differentiated land management histories. To date the sediments, we established a multi-proxy chronostratigraphy based on radiocarbon dating, optical stimulated luminescence and archaeology. In addition, we used the reconstructed land management history of the two catchments and aerial photographs. The reconstructed soil histories of the two catchments suggest a close link between agricultural land management practices and soil erosion in the last seven millennia. Notably, results indicate that soil erosion activity started earlier in Montefrío, corresponding to the first human settlements seven millennia ago. However, in the last two millennia erosion and gully activity in Baena have rapidly overtaken that of the Montefrío due to a more intense economic and agricultural activity of the former. In the last decades, erosion and gully activity in both catchments have been very high due to the intensification of the olive cultivation.

A water-energy-food-carbon nexus optimization model for sustainable agricultural development in the Yellow River Basin under uncertainty

Water scarcity, limited land resources and global warming are the most challenging issues facing sustainable agricultural development, and modern agriculture requires efficient and environmentally friendly agricultural water and land management practices. This paper proposes a water-energy-food-carbon nexus system optimization model, which aims to formulate a scientific and rational water and land resource allocation strategy for sustainable agricultural development to increase irrigation water productivity, reduce carbon emissions and enhance regional agriculture competitiveness. First, the carbon footprint life cycle assessment method was used to measure carbon sinks and carbon emissions across the ecosystem. Second, a multiobjective nonlinear programming model with the goal of maximizing irrigation water productivity, minimizing carbon emissions, and maximizing the competitiveness of low-carbon agriculture is established. Finally, an empirical study is performed in the Yellow River Basin, and an elitist nondominated sorting genetic algorithm is used to solve the model. The research results show that the water-energy-food-carbon nexus system optimization model based on uncertain conditions established in this paper, can maximize resource utilization efficiency and effectively measure the impact of regional agricultural production on the environment. It has reference significance for the realization of carbon neutrality in the region.

Extraction of Saline Soil Distributions Using Different Salinity Indices and Deep Neural Networks

Soil salinization has become one of the major environmental problems threatening food security. The identification and knowledge of the spatial distributions of soil salinization are key in addressing this problem. This study assumes that a good saline land identification effect can be obtained with the help of deep learning methods. Therefore, this study used the OLI sensor data from the Landsat-8, based on the U2-Network, and proposes a method to extract saline land from remote sensing images. The study also adds different salinity indices (SI, SI1, and SI2) to explore its impact on classification accuracy. Through our method, accurate saline soil distribution information were obtained, and several verification indicators (the Intersection-over-Union (IoU), recall, precision, and F1-score) were all measured above 0.8. In addition, compared with the multi-spectral training results, the classification accuracy increased after adding a specific salinity index, and most of the accuracy indices increased by about 2% (the IoU increased by 3.70%, recall increased by 1.50%, precision increased by 2.81%, and F1-score increased by 2.13%). In addition, we also included a case study based on our methodology to analyze the distribution characteristics and changes of saline soil in the Zhenlai area of Northeast China from 2016 to 2020. We found that the area of saline land in the Zhenlai area has reduced, which shows that the extraction method proposed in this study is feasible. Overall, this paper indicates that deep learning-based methods can efficiently extract the salinity of soil and enhance the mapping of its spatial distribution. The study has the broad impact of supplementing satellite imagery for salinity modeling and helping to guide agricultural land management practices for northeastern China and other salinized regions.

Sustainable Agricultural Management of Land Using Technology for Soil and Water Conservation within the Central Rift Valley, Central Ethiopia

Ethiopia has used several techniques for conserving water and soil. However, different sociocultural and technical problems have been affecting their implementation. The study is aimed at assessing sustainable agricultural management of land implementations through traditional and modern conservation of soil and water technologies in the central Rift Valley, Ethiopia. The research approach used was a descriptive survey using a cross-sectional research design. Household heads were chosen at random from the three kebeles, while representatives were chosen using a proportionate sample technique. Furthermore, kebeles and key informants were selected by using a purposive sampling technique. Data were collected through questionnaires, key informant interviews, and field observation. The results show that both traditional and contemporary methods for conserving soil and water have advantages and disadvantages of their own, and some of them have been combined. A number of factors, including age, family size, education, topography, distance from the homestead, income, and the availability of training, have a big impact on whether or not soil and water conservation methods are adopted. The majority of farmers use mixed farming followed by crop production to meet their livelihood needs. The most widely implemented physical measure was terracing, followed by stone bunds. The farmers practiced traditional waterways, furrows, check dams, terracing, and stone bunds as traditional conservation practices. Agroforestry, followed by grass strip and area closure, was the most commonly implemented vegetative measure. Besides, animal manure, followed by animal parking, was the most implemented agronomic measure. The concerned stakeholders need to pay more attention to community mobilization for the conservation, upkeep, and development of traditional and modern soil and water conservation structures. In order to employ traditional and contemporary soil and water conservation measures for sustainable agricultural land management practices, experts need to instruct the local farmers.

Albedo impacts of current agricultural land use: Crop-specific albedo from MODIS data and inclusion in LCA of crop production

Agricultural land use and management practices affect the global climate due to greenhouse gas (GHG) fluxes and changes in land surface properties. Increased albedo has the potential to counteract the radiative forcing and warming effect of emitted GHGs. Thus considering albedo could be important to evaluate and improve agricultural systems in light of climate change, but the albedo of individual practices is usually not known. This study quantified the albedo of individual crops under regional conditions, and evaluated the importance of albedo change for the climate impact of current crop production using life cycle assessment (LCA). Seven major crops in southern Sweden were assessed relative to a land reference without cultivation, represented by semi-natural grassland. Crop-specific albedo data were obtained from a MODIS product (MCD43A1 v6), by combining its spatial response pattern with geodata on agricultural land use 2011–2020. Fluxes of GHGs were estimated using regional data and models, including production of inputs, field operations, and soil nitrogen and carbon balances. Ten-year mean albedo was 6–11% higher under the different crops than under the reference. Crop-specific albedo varied between years due to weather fluctuations, but differences between crops were largely consistent. Increased albedo countered the GHG impact from production of inputs and field operations by 17–47% measured in GWP100, and the total climate impact was warming. Using a time-dependent metric, all crops had a net cooling impact on global mean surface temperature on shorter timescales due to albedo (3–12 years under different crops), but a net warming impact on longer timescales due to GHG emissions. The methods and data presented in this study could support increasingly comprehensive assessments of agricultural systems. Further research is needed to integrate climatic effects of land use on different spatial and temporal scales, and direct and indirect consequences from a systems perspective.

Tradeable Nitrogen Abatement Practices for Diffuse Agricultural Emissions: A \u2018Smart Market\u2019 Approach

Markets in pollution permits for managing environmental quality have been advocated by economists since early 1970s as a mechanism that can deliver pollution reduction targets at lower cost to regulated entities than traditional uniform command-and control approaches. This study explores whether a ‘smart market’ cap-and-trade scheme between non-point sources can offer meaningful, robust and policy amenable, advantages over alternative approaches for nitrogen management in a realistic setting: 6504 individual farms in Limfjorden catchment, Denmark. The scheme involves multilateral trading of nitrogen emission rights among farms via changes in agricultural land management practices under a catchment-level cap on total nitrogen load. In this, the first exploration of non-point to non-point smart market nitrogen trading in a real setting, we estimate efficiency gains compared to uniform command-and-control regulation, explore the robustness of these gains in the face of non-participation, and reflect on farmers’ potential acceptance of the trading market in comparison with its command-and-control analog: spatially-targeted regulation, implemented via location-specific limits on nitrogen leaching. Results indicate that the smart market has the potential to substantially reduce the cost of meeting the catchment’s nitrogen reduction target. For a 21.5% reduction from baseline nitrogen load, the market delivers cost savings of 56% (DKK273 million, €36.6 million) compared to uniform regulation, with participating farms realising a mean net benefit of DKK 723/ha (€ 97/ha). Market performance is relatively robust against transaction cost; when delivering a 21.5% reduction in nitrogen load to Limfjorden, approximately 70% of the overall efficiency gain could be retained if only 24% of farms engaged with the market.

Climate Change Impacts on Agricultural Water Availability in the Middle Rio Grande Basin

AbstractWe present a comprehensive analysis of water availability under plausible future climate conditions in a heavily irrigated agricultural watershed located in the middle section of the Rio Grande Basin in the United States Desert Southwest. Future managed streamflow scenarios (through year 2099) were selected from among 97 scenarios developed based on downscaled, bias‐corrected global climate model outputs to evaluate future inflows to the principal surface water storage reservoirs, possible future reservoir releases, and groundwater pumping to sustain irrigated agriculture. The streamflow projections describe a wide range of dry and wet conditions compared to the average historical flows in the river, indicating significant uncertainty in future water availability in the Rio Grande Basin. We applied the Soil and Water Assessment Tool to illustrate the impact of climate futures on different components of the water budget at a watershed scale. Results indicate declining reliability of reservoir storage to meet the water demand of irrigated agriculture. The impact of declining surface water can be offset by increasing the pressure on the already‐strained groundwater resources. However, the region should be prepared to use slightly saline (total dissolved solids [TDS] &gt; 1,000 mg/L) and moderately saline groundwater (TDS &gt; 3,000 mg/L) as fresh groundwater in the regional aquifer is depleted within the 21st Century under hotter and drier conditions and status quo agricultural land and water management practices.

Farmers’ perceived effectiveness of indigenous land management practices: a study in semi-arid Ghana

ABSTRACT Indigenous agricultural land management practices have potentials for soil fertility maintenance and balancing crop nutrient requirements. However, the contribution of indigenous farmland management practices and consequential effects on food production has not been fully addressed in the literature. This study assesses rural farmers’ perceived effectiveness of indigenous farmland management practices in semi-arid areas of Ghana. The study employs quantitative methods using interview schedules with farming households and stakeholders in the agricultural sector to generate primary data. The sample consists of 226 heads of farming households from three farming communities and three stakeholders – an agricultural extension officer, a representative of a famer-based organisation and an officer of a non-governmental organisation. The results of the study reveal that multi-cropping, inter-cropping, and animal manure application are the primary indigenous techniques for managing farmlands by farmers. Also, farmers use some modern farming practices, such as improved seeds, composting, inorganic fertilisers and agroforestry. The assessment shows that farmers who combine both practices perceive their crop yields to be higher than those who practice only the modern techniques. The study recommends that priority should be given to educating rural farmers on environmentally-friendly farming techniques by combining useful indigenous agricultural land management practices with modern practices.

Data fusion of visible near-infrared and mid-infrared spectroscopy for rapid estimation of soil aggregate stability indices

Soil aggregate stability (AS) is a crucial soil physical property for sustainable agricultural and environmental land management practices. Yet, its conventional laboratory methods are fastidious and time consuming thereby restricting high-density sampling and large-scale estimation. Therefore, the potential of fusing visible near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy to enhance the prediction of three AS indices, namely, fast wetting (FW), slow wetting (SW) and mechanical breakdown (MB) on some Belgian Retisol, Cambisol and Luvisol topsoils was evaluated. Partial least squares regression (PLSR) was used to build calibration models for the three AS indices using the vis-NIR spectra, MIR spectra and the fusion of both spectra (SF). Another data fusion approach (i.e. model output averaging, MOA) included the use of four averaging algorithms. Results showed that MIR models outperformed the vis-NIR models for all three indices. The SF approach showed an improved prediction performance over both individual sensor techniques for the FW index only. MOA models outperformed the individual and SF models and yielded the best prediction accuracy for SW and MB indices. Data fusion modelling thus enhanced the accuracy of FW, SW and MB predictions, although the selection of the best data fusion approach is dependent on the nature of the dataset and the stability index to be assessed.