Combined application of Artificial Neural Networks and life cycle assessment in lentil farming in Iran

AbstractIn this study, an Artificial Neural Network (ANN) was applied to model yield and environmental emissions from lentil cultivation in Esfahan province of Iran. Data was gathered from lentil farmers using face to face questionnaire method during 2014–2015 cropping season. Life cycle assessment (LCA) was applied to investigate the environmental impact categories associated with lentil production. Based on the results, total energy input, energy output to input ratio and energy productivity were determined to be 32,970.10MJha−1, 0.902 and 0.06kgMJ−1, respectively. The greatest amount of energy consumption was attributed to chemical fertilizer (42.76%). Environmental analysis indicated that the acidification potential was higher than other environmental impact categories in lentil production system. Also results showed that the production of agricultural machinery was the main hotspot in abiotic depletion, eutrophication, global warming, human toxicity, fresh water aquatic ecotoxicity, marine aquatic ecotoxicity and terrestrial ecotoxicity impact categories, while direct emissions associated with lentil cultivation was the main hotspot in acidification potential and photochemical oxidation potential. In addition, diesel fuel was the main hotspot only in ozone layer depletion. The ANN model with 9-10-6-11 structure was identified as the most appropriate network for predicting yield and related environmental impact categories of lentil cultivation. Overall, the results of sensitivity analysis revealed that farmyard manure had the greatest effect on the most of the environmental impacts, while machinery was the most affecting parameter on the yield of the crop

Capacity-building activities related to climate change vulnerability and adaptation assessment and economic valuation for Fiji

The Terms of Reference for this work specified three objectives to the Fiji component: Objective 1a: to provide a prototype FIJICLIM model (covered under PICCAP funding) Objective 1b: to provide training and transfer of FIJICLIM Objective 1c: to present and evaluate World Bank study findings and to identify future directions for development and use of FIJICLIM (2-day workshop) Proceedings of the training course and workshop were prepared by the Fiji Department of Environment. The summaries from these proceedings reflect a very high degree of success with the contracted activities

The macroeconomic cost of catastrophic pollinator declines

We develop a computable general equilibrium (CGE) approach to assess the macroeconomic impacts of productivity shocks due to catastrophic losses of pollination ecosystem services at global and regional scales. In most regions, producers of pollinator dependent crops end up benefiting because direct output losses are outweighed by increased prices, while non-agricultural sectors experience large adverse indirect impacts, resulting in overall losses whose magnitudes vary substantially. By comparison, partial equilibrium analyses tend to overstate the costs to agricultural producers, understate aggregate economy-wide losses, and overstate the impacts on consumers' welfare. Our results suggest an upper bound on global willingness to pay for agricultural pollination services of $127–$152 billion

The Economic Case for Landscape Restoration in Latin America

Degraded lands—lands that have lost some degree of their natural productivity through human activity—account for over 20 percent of forest and agricultural lands in Latin America and the Caribbean. Some 300 million hectares of the region's forests are considered degraded, and about 350 million hectares are now classified as deforested. The agriculture and forestry sectors are growing and exerting great pressure on natural areas. With the region expected to play an increasingly important role in global food security, this pressure will continue to ratchet up. In addition, land degradation is a major driver in greenhouse gas emissions in the region. Forest and landscape restoration can offer a solution to these increasing pressures

Numerical study of a passive solar still with separate condenser

A passive solar still with separate condenser has been modeled and its performance evaluated. The system has one basin in the evaporation chamber and two basins (middle and upper) in the condenser chamber, with a glass cover over the evaporator basin and an opaque condensing cover over the upper basin. The evaporator, middle and upper basins yield the first, second and third effects respectively. The top part of the condensing cover is shielded from solar radiation to keep the cover relatively cool. Water vapor from the first effect condenses under the glass cover while the remainder of it flows into the condenser, by purging and diffusion, and condenses under the liner of the middle basin. The performance of the system is evaluated and compared with that of a conventional solar still under the same meteorological conditions. Results show that the distillate productivity of the present still is 62% higher than that of the conventional type. Purging is the most significant mode of vapor transfer from the evaporator into the condenser chamber. The first, second and third effects contribute 60, 22 and 18% of the total distillate yield respectively. It is also found that the productivity of the solar still with separate condenser is sensitive to the absorptance of the evaporator basin liner, mass of water in the evaporator and middle basins, and wind speed. The mass of water in the upper basin has a marginal effect on distillate production. Other results are presented and discussed in detail

Sensitivity of a high‐elevation rocky mountain watershed to altered climate and CO2

We explored the hydrologic and ecological responses of a headwater mountain catchment, Loch Vale watershed, to climate change and doubling of atmospheric CO2 scenarios using the Regional Hydro‐Ecological Simulation System (RHESSys). A slight (2°C) cooling, comparable to conditions observed over the past 40 years, led to greater snowpack and slightly less runoff, evaporation, transpiration, and plant productivity. An increase of 2°C yielded the opposite response, but model output for an increase of 4°C showed dramatic changes in timing of hydrologic responses. The snowpack was reduced by 50%, and runoff and soil water increased and occurred 4–5 weeks earlier with 4°C warming. Alpine tundra photosynthetic rates responded more to warmer and wetter conditions than subalpine forest, but subalpine forest showed a greater response to doubling of atmospheric CO2 than tundra. Even though water use efficiency increased with the double CO2 scenario, this had little effect on basin‐wide runoff because the catchment is largely unvegetated. Changes in winter and spring climate conditions were more important to hydrologic and vegetation dynamics than changes that occurred during summer

The effects of climate change and variation in New Zealand: An assessment using the CLIMPACTS system

Along with a need to better understand the climate and biophysical systems of New Zealand, the need to develop an improved capacity for evaluating possible changes in climate and their effects on the New Zealand environment has been recognised. Since the middle of 1993 the CLIMPACTS programme, has been focused on the development of such a capacity, in the first instance for the agricultural sector. the goals of this present assessment are: 1. To present current knowledge on likely scenarios of climate change and associated uncertainties in New Zealand; 2. To present current knowledge, based on quantitative analyses using a consistent set of scenarios, on the likely effects of climate change on a range of agricultural and horticultural crops of economic importance; 3. To demonstrate, by way of this report and the associated technical report, the capacity that has been developed for ongoing assessments of this kind in New Zealand. This report has been prepared for both the science and policy communities in New Zealand. There are two main components: 1. The detailed findings of the assessment, presented in a series of chapters; 2. An annex, which contains technical details on models used in the assessment

Assessment of the potential impacts of plant traits across environments by combining global sensitivity analysis and dynamic modeling in wheat

A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites x 125 years), management practices (3 sowing dates x 2 N fertilization) and $CO_2$ (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait x environment x management landscape ($\sim$ 82 million individual simulations in total). The patterns of parameter x environment x management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identifcation of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.Comment: 22 pages, 8 figures. This work has been submitted to PLoS On

Impact of droughts on the carbon cycle in European vegetation : a probabilistic risk analysis using six vegetation models

Peer reviewedPublisher PD

Integrating remote sensing datasets into ecological modelling: a Bayesian approach

Process-based models have been used to simulate 3-dimensional complexities of forest ecosystems and their temporal changes, but their extensive data requirement and complex parameterisation have often limited their use for practical management applications. Increasingly, information retrieved using remote sensing techniques can help in model parameterisation and data collection by providing spatially and temporally resolved forest information. In this paper, we illustrate the potential of Bayesian calibration for integrating such data sources to simulate forest production. As an example, we use the 3-PG model combined with hyperspectral, LiDAR, SAR and field-based data to simulate the growth of UK Corsican pine stands. Hyperspectral, LiDAR and SAR data are used to estimate LAI dynamics, tree height and above ground biomass, respectively, while the Bayesian calibration provides estimates of uncertainties to model parameters and outputs. The Bayesian calibration contrasts with goodness-of-fit approaches, which do not provide uncertainties to parameters and model outputs. Parameters and the data used in the calibration process are presented in the form of probability distributions, reflecting our degree of certainty about them. After the calibration, the distributions are updated. To approximate posterior distributions (of outputs and parameters), a Markov Chain Monte Carlo sampling approach is used (25 000 steps). A sensitivity analysis is also conducted between parameters and outputs. Overall, the results illustrate the potential of a Bayesian framework for truly integrative work, both in the consideration of field-based and remotely sensed datasets available and in estimating parameter and model output uncertainties