Wetland Water-Level Prediction in the Context of Machine-Learning Techniques: Where Do We Stand?

Wetlands are simply areas that are fully or partially saturated with water. Not much attention has been given to wetlands in the past, due to the unawareness of their value to the general public. However, wetlands have numerous hydrological, ecological, and social values. They play an important role in interactions among soil, water, plants, and animals. The rich biodiversity in the vicinity of wetlands makes them invaluable. Therefore, the conservation of wetlands is highly important in today’s world. Many anthropogenic activities damage wetlands. Climate change has adversely impacted wetlands and their biodiversity. The shrinking of wetland areas and reducing wetland water levels can therefore be frequently seen. However, the opposite can be seen during stormy seasons. Since wetlands have permissible water levels, the prediction of wetland water levels is important. Flooding and many other severe environmental damage can happen when these water levels are exceeded. Therefore, the prediction of wetland water level is an important task to identify potential environmental damage. However, the monitoring of water levels in wetlands all over the world has been limited due to many difficulties. A Scopus-based search and a bibliometric analysis showcased the limited research work that has been carried out in the prediction of wetland water level using machine-learning techniques. Therefore, there is a clear need to assess what is available in the literature and then present it in a comprehensive review. Therefore, this review paper focuses on the state of the art of water-level prediction techniques of wetlands using machine-learning techniques. Nonlinear climatic parameters such as precipitation, evaporation, and inflows are some of the main factors deciding water levels; therefore, identifying the relationships between these parameters is complex. Therefore, machine-learning techniques are widely used to present nonlinear relationships and to predict water levels. The state-of-the-art literature summarizes that artificial neural networks (ANNs) are some of the most effective tools in wetland water-level prediction. This review can be effectively used in any future research work on wetland water-level prediction.publishedVersio

Modelling potential soil erosion and sediment delivery risk in plantations of Sri Lanka

The current trend in agricultural practices is expected to have a detrimental impact in terms of accelerating soil erosion. Assessment of the cumulative impact of various management strategies in a major plantation is a measure of the sustainably of soil resources. Thus, the current study aimed to develop the potential soil erosion map for a selected plantation (8734 ha in size) in tropical Sri Lanka using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Sediment Delivery Ratio (SDR) model. The estimated mean annual soil loss rate of the selected plantation was 124.2 t ha−1 ranging from 0.1 to 6903.3 t ha−1. Out of the total extent, ~49.5% of the area belongs to the low soil erosion hazard category (0–5 t ha−1 year−1) while ~7.8% falls into very high (25–60 t ha−1 year−1) and ~1.3% into extremely high (60 < t ha−1 year−1) soil erosion hazard classes. The rainfall erosivity factor (R) for the entire study area is 364.5 ± 98.3 MJ mm ha−1 hr−1. Moreover, a relatively higher correlation was recorded between total soil loss and R factor (0.3) followed by C factor (0.2), P factor (0.2), LS factor (0.1), and K factor (<0.1). It is evident that rainfall plays a significant role in soil erosion in the study area. The findings of this study would help in formulating soil conservation measures in the plantation sector in Sri Lanka, which will contribute to the country’s meeting of the UN Sustainable Development Goals (SDGs)

Climate Change and Soil Dynamics: A Crop Modelling Approach

The impact of global climate change is a challenge to the sustainability of many ecosystems, including soil systems. However, the performance of soil properties under future climate was rarely assessed. Therefore, this study was carried out to evaluate selected soil processes under climate change using an agri-environmental modeling approach to Sri Lanka. The Agricultural Production Systems Simulator (APSIM) model was used to simulate soil and plant-related processes using recent past (1990–2019) and future (2041–2070) climates. Future climate data were obtained for a regional climate model (RCM) under representative concentrations pathway 4.5 scenarios. Rainfalls are going to be decreased in all the tested locations under future climate scenarios while the maximum temperature showcased rises. According to simulated results, the average yield reduction under climate change was 7.4%. The simulated nitrogen content in the storage organs of paddy declined in the locations (by 6.4–25.5%) as a reason for climate change. In general, extractable soil water relative to the permanent wilting point (total available water), infiltration, and biomass carbon lost to the atmosphere decreased while soil temperature increased in the future climate. This modeling approach provides a primary-level prediction of soil dynamics under climate change, which needs to be tested using fieldwork

The potential of Bambara groundnut: An analysis for the People’s Republic of China

While China has transformed its economy over recent decades, challenges such as climate change and land degradation have continued to impact its agriculture. These effects along with changes in diets and growing food imports will force China to look for alternative cropping options. Despite the broad potential of Bambara groundnut (Vigna subterranea L. Verdc) as a resilient and nutritious underutilized crop, less is known about its potential in Asia. Here, we explore the potential of Bambara groundnut to become a mainstream crop in mainland China. A suitability analysis is presented for Bambara groundnut to examine the degree of seasonal adaptability of this crop against its climate and soil requirements across China. Results showed that the crop has yield potential in areas that can be too marginal for production of other mainstream crops such as soybean (Glycine max). If realized, the potential of Bambara groundnut could contribute to China's agriculture and reduce its reliance on vegetable protein imports. Using an average seasonal potential yield of 0.85 t/ha over a potential available area of between 55 and 112 million ha (based on 4 land availability scenarios) and modest price of 143 USD/t, yearly income between USD 6 and 13 billion can potentially be contributed by widespread cultivation of this crop. As well as food security, this drought-resistant nitrogen-fixing legume could also contribute to land rehabilitation, particularly in the areas where shift in planting dates and land degradation is noticeable. This study demonstrates the need for more investment and research into adoption of Bambara groundnut and other underutilized crops that have the potential to transform agriculture in populous Asian countries

A Shortlisting Framework for Crop Diversification in the United Kingdom

We present a systematic framework for nationwide crop suitability assessment within the UK to improve the resilience in cropping systems and nutrition security of the UK population. An initial suitability analysis was performed using data from 1842 crops at 2862 grid locations within the UK, using climate (temperature and rainfall) and soil (pH, depth, and texture) data from the UK Met Office and British Geological Survey. In the second phase, additional qualitative and quantitative data are collected on 56 crops with the highest pedoclimatic suitability and coverage across the UK. An exercise was conducted on crops within each category using a systematic ranking methodology that shortlists crops with high value across a multitude of traits. Crops were ranked based on their nutritional value (macronutrients, vitamins, and minerals) and on adaptive (resistance to waterlogging/flood, frost, shade, pest, weed, and diseases and suitability in poor soils) and physiological traits (water-use efficiency and yield). Other characteristics such as the number of special uses, available germplasm through the number of institutions working on the crops, and production knowledge were considered in shortlisting. The shortlisted crops in each category are bulbous barley (cereal), colonial bentgrass (fodder), Russian wildrye (forage), sea buckthorn (fruit), blue lupin (legume), shoestring acacia (nut), ochrus vetch (vegetable), spear wattle (industrial), scallion (medicinal), and velvet bentgrass (ornamental/landscape). These crops were identified as suitable crops that can be adopted in the UK. We further discuss steps in mainstreaming these and other potential crops based on a systematic framework that takes into account local farming system issues, land suitability, and crop performance modelling at the field scale across the UK

Energy Balance Assessment in Agricultural Systems; An Approach to Diversification

The energy in agricultural systems is two-fold: transformation and utilization. The assessment and proper use of energy in agricultural systems is important to achieve economic benefits and overall sustainability. Therefore, this study was conducted to evaluate the energy balance of crop and livestock production, net energy ratio (NER), and water use efficiency (WUE) of crops of a selected farm in Sri Lanka using the life cycle assessment (LCA) approach. In order to assess the diversification, 18 crops and 5 livestock types were used. The data were obtained from farm records, personal contacts, and previously published literature. Accordingly, the energy balance in crop production and livestock production was −316.87 GJ ha−1 Year−1 and 758.73 GJ Year−1, respectively. The energy related WUE of crop production was 31.35 MJ m−3. The total energy balance of the farm was 736.2 GJ Year−1. The results show a negative energy balance in crop production indicating an efficient production system, while a comparatively higher energy loss was shown from the livestock sector. The procedure followed in this study can be used to assess the energy balance of diversified agricultural systems, which is important for agricultural sustainability. This can be further developed to assess the carbon footprint in agricultural systems

Crop model ideotyping for agricultural diversification

Evidence based crop diversification requires modelling for crops that are currently neglected or underutilised. Crop model calibration is a lengthy and resource consuming effort that is typically done for a particular variety or a set of varieties of a crop. Whilst calibration data are widely available for major crops, such data are rarely available for underutilised crops due to limited funding for detailed field data collection and model calibration. Subsequently, the lack of evidence on their performance will lead to the lack of interest from the policy and regulatory communities to include these crops in the agricultural development plans. In order to motivate further research into the use of state of the art techniques in modelling for less known crops, we have developed and validated an ideotyping technique that approximates the crop modelling parameters based on already calibrated crops of different lineage. The method has been successfully tested for hemp (Cannabis sativa L.) based on a well-known crop model. In this paper we present the method and provide an impetus on the way forward to further develop such methods for modelling the performance of minor crops and their varieties. • The approach works based on modelling the performance of hemp using the knowledge from an existing model that was developed for sugar cane. • The customisation uses one of the most prominent models (AquaCrop) to approximate growth coefficients for hemp (Cannabis sativa L.). • A sequential procedure was used to approximate the phenological stages in the growth model that performs well in the calibration and validation steps

Process-Based Crop Models in Soil Research: A Bibliometric Analysis

Different types of soil data are used in process-based crop models as input data. Crop models have a diverse range of applications, and soil research is one of them. This bibliographic analysis was conducted to assess the current literature on soil-related applications of crop models using two widely used crop models: Agricultural Production Systems Simulator (APSIM) and Decision Support System for Agrotechnology Transfer (DSSAT). The publications available in the Scopus database during the 2000–2021 period were assessed. Using 523 publications, a database on the application of process-based crop models in soil research was developed and published in an online repository, which is helpful in determining the specific application in different geographic locations. Soil-related applications on APSIM and DSSAT models were found in 41 and 43 countries, respectively. It was reported that selected crop models were used in soil water, physical properties, greenhouse gas emissions, N leaching, nutrient dynamics, and other physical and chemical properties related to applications. It can be concluded that a crop model is a promising tool for assessing a diverse range of soil-related processes in different geographic regions