Sustainable Agriculture for Climate Change Adaptation

Evaluating the potential use of greenhouse gas mitigation techniques in on-farm rice cultivation: a case study in Thailand. At the 21st Conference of the Parties to the United Nations on Climate Change (COP21), an important agreement was reached between the countries that are part of the United Nations Framework Convention on Climate Change (UNFCCC).

Materials and Methods 1. Mitigation Technique Selection

After the last harvest year for data collection, in 2017 the farmer assessment survey took place for each farm. A multi-criteria evaluation was developed to rate farmers in the qualitative evaluation of the mitigation techniques.

Figure 1. System boundary from cradle to farm gate of the study (adapted from Arunrat et al

Results and Discussion

The effect on rice yield of each mitigation technique was the priority of the farmers. Consequently, farmers' yield perception was one of the most important variables influencing their decision-making.

Table 3. Average abatement cost (AAC) using different mitigation techniques (Authors own calculation).

Conclusions

Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Metz, B., Davidson, O.R., Bosch, P.R., Dave, R., Meyer, L.A., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2007; pp. IPCC. The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2013.

Farmers’ Net Income Distribution and Regional

Vulnerability to Climate Change: An Empirical Study of Bangladesh

Introduction

Given the impacts of climate change, resource constraints and competitive demands, agricultural and food systems continue to face significant challenges. The aim of this study was to project the poverty impacted by climate change on crop production and to provide possible adaptive measures.

Review of the Literature

Furthermore, most of the previous studies on the impact of climate change on agricultural production have been for specific regions. However, a comprehensive study of climate change impacts comparing the regions of Bangladesh could be hugely significant.

Methodology 1. Survey Data

We estimate the incomes of all households in the sample based on the assumption of climate change impacts and plot the distribution of the estimated incomes, assuming that the distribution follows the log-normal distribution. We then project the crop yield loss based on the assumption of the literature searches and estimate the projected income per capita.

Figure 1. Map of the objective regions of Bangladesh.

Results and Discussion

Figure 10 shows the annual per capita income (actual and projected) in US dollars of sample households in Bangladesh. In addition, the probability density of the low-income range increases in the projected income distribution when rice yield loss due to climate change is considered.

Table 2 shows descriptive statistics of income status by region. Poverty rates were estimated by applying the poverty line and the purchasing power parity from the World Bank [22] to log-normal income distributions

Conclusions

However, adaptation techniques in agriculture are an important tool to avoid the negative effects of climate change [117]. Perceptions of climate change and local adaptation strategies of at-risk rural households in Bangladesh. Climate. Regional vulnerability to climate change affects Asian rice production and options for adaptation. Adv.

Effects of Climate Change on Crop Production and Climate Adaptive Techniques for Agriculture in Bangladesh.Soc. Exploring the relationship between climate change and rice yield in Bangladesh: A time series data analysis. Agric. Assessing the effect of climate change on boro rice production in Bangladesh using the DSSAT model.J.

Crop yield responses to climate change in the Huang-Huai-Hai Plain of China.Agric.

Table A1. Household income (US$/yr.) from different sources, by region.

The Value of Tactical Adaptation to El Niño–Southern Oscillation for East Australian Wheat

Materials and Methods 1. Climatic Data

For the various scenarios, yield differences and changes in gross margin compared to the base scenario and fixed adaptation scenarios have been calculated per year. The last frost day with a 10% chance of frost was usually earlier in SOI stages II and IV, and delayed in SOI stages I and III, mostly in the eastern locations (Figure S9). In contrast, long-term mean yields in SOI stages I and III were generally lower, ranging from 0.90 to 2.70 t ha−1 across sites, and averaging 2.00 t ha−1 for Eastern Australia.

Simulated mean yield in the baseline scenario for all years and years from each of the three SOI classes at 15 sites across the Eastern Wheatbelt and for the entire Eastern Wheatbelt region. Compared to the baseline, the fixed adaptation scenario increased yield in most years at all sites, although yield losses were also observed in a few years at all sites (Figure 4 and Figure S10). In Australia, SOI phases affect climate and crops most in the eastern part of the wheat belt.

Zheng, B.; Chenu, K.; Doherty, A.; Chapman, S. The APSIM-Wheat Module (7.5 R3008); Agricultural Production Systems Simulator (APSIM) Initiative: Toowoomba, Australia, 2014.

Figure 1. Map of the seven regions of the East Australian wheatbelt, with 15 sites chosen to represent those regions

Possible Scenarios of Winter Wheat Yield Reduction of Dryland Qazvin Province, Iran,

Materials and Methods

The province is influenced by Siberian and Mediterranean winds, which are significantly important factors in controlling the climate of the province. The total yield of winter wheat in the province is 445 million kg, of which 364 million kg (82%) belongs to irrigated agriculture and 80.7 million kg (18%) to dry farming. The average dryland winter wheat yield in the province is estimated to be 1541 kg ha−1.

The daily average temperature and precipitation data for 32 years were collected from the six meteorological stations in the province (Figure 1). The calibration of the stations (points) against the grid cells (pixels) was done by downscaling the SDSM linear regression model. Figures 2 and 3 show the observed versus the simulated values ​​of the temperature and precipitation for the years 2006–2015.

Meanwhile, since 26 synoptic variables are considered predictor variables in these models, logically having a unique equation was not possible due to the accumulated error.

Figure 1. Map of the studied area.

2EVHUYHG UFS

Results

However, the rcp2.6 scenario assumes a less downward trend in annual precipitation for the period 2070–2099. The analysis of variance results showed greater efficiency for the RCP scenarios than the A and B scenarios in predicting the region's daily mean temperature (Table 6), because there was no statistically significant difference between the temperature values ​​simulated by the RCPs and the observed values ​​(atp < 0.01), while the temperature values ​​simulated by A and B differed significantly from the observed values ​​(atp < 0.01). The results of the analysis of variance indicated that all scenarios were efficient enough to predict the region's annual precipitation (Table 7), as no statistically significant difference was found between the simulated and observed values ​​(atp < 0.01).

In addition, the CanESM2 scenarios simulated annual precipitation values ​​that are closer to the observed values ​​than the HadCM3 scenarios (Table 8). Together, these indicators showed relatively higher performance for the CanESM2 scenarios than the HadCM3 scenarios in predicting mean daily temperature and annual precipitation in the region. Results of the assessment of the effectiveness of the used scenarios for forecasts of average daily temperature.

The results of regression analysis and Pearson correlation test showed that rainfall in March was the most effective factor for the dryland winter wheat yield of the region (Table 9).

Table 2. Results of the daily mean temperature predictions of the CanESM2 scenarios for the periods 2010–2039, 2040–2069, and 2070–2099.

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These reductions in yield are consistent with the reductions in average precipitation in March during the three prospective periods (Figure 4).

Discussion

The CanESM2 scenarios postulated a higher variability in predicted temperature values ​​than the HadCM3 scenarios. All scenarios, except B2, revealed that there would be a reduction in annual precipitation in all investigated periods. This could be another plausible reason for the increase (14%) in annual precipitation predicted by rcp2.6.

In the province of Qazvin, dryland winter wheat in March is at the tillage stage (personal communication with farmers). Furthermore, the observed decreases in March precipitation over the three future periods may have been due to shifts in seasons due to warmer temperatures in the areas affected by the study region. In general, the region's daily mean temperature tended to increase and annual precipitation tended to decrease during the three prospective periods studied.

However, scenarios rcp2.6 and B2 respectively predicted that precipitation would decrease less or even increase in the third period.

New Breeding Techniques for Greenhouse Gas (GHG) Mitigation: Plants May Express Nitrous

• Introduction—Nitrous Oxide Continues to Bloom Unabated
• Combating GHGs: Current N 2 O Mitigation Strategies and Limitations
• Nitrous Oxide Reductase—An Orphaned Soil Protein?
• Catch Me If You Can: Can Plants Catalytically Convert N 2 O in planta?
• Novel Breeding Task: “Gas Cracking” Plants

This gene encodes the enzyme nitric oxide reductase (N2OR), an oxidoreductase that catalyzes the removal of N2O from the atmosphere, a process naturally carried out by denitrifying and non-nitrifying soil bacteria [98]. With the search terms "nitric oxide reductase" and "plant", scientific records show that soil microbiologists are increasingly interested in the movement of N into the atmosphere (Figure 4). The importance of denitrifiers without genes encoding nitric oxide reductase for N2O emissions from soil. Glob.

Effect of increased efficiency fertilizers on nitrous oxide emissions and crop yields: A meta-analysis. Soil Sci. Impact of urease and nitrification inhibitors on nitrous oxide emissions from fluvo-water soils in the North China Plain. Biol. Biological nitrification inhibition by Brachiaria grasses reduces soil nitrous oxide emissions from cattle urine patches. Soil Biol.

Nitric oxide reductase catalytic cycle - The enzyme that catalyzes the final step of denitrification. J.

Figure 1. GHG levels since 1850. The green line represents the increase in CO 2 concentration since 1850;

The Nexus of Weather Extremes to Agriculture Production Indexes and the Future Risk in Ghana

Methodology

Therefore, there is a difference in the degree of trend in different seasons of maximum annual rainfall. Thep-value of Kendall's test of seasonal trend, p<0.001, indicating that it is statistically significant. Determining the marginal distribution by maximizing the negative log-likelihood of the GEV for the highest annual temperature leads to the following function, equation (2):(μ,σ,γ)of.

In addition, most cattle bred in Ghana are more adaptable to coastal conditions. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Forum on Climate Change; IPCC: Geneva, Switzerland, 2014;. Nair, K.P.P. Agronomy and economics of important tree crops in the developing world; Elsevier: Amsterdam, The Netherlands, 2010; ISBN 0123846781.

An evaluation of the use of partial least squares structural equation modeling in marketing research.J.

Figure 3 shows the primary crop food calendar.

Geographic Information and Communication Technologies for Supporting Smallholder

• Geographic Information in Agriculture
• Results 1. AGI Initiatives
• Future Potential of AGI
• Summary

Duncombe [35] also analyzed mobile phone use for agriculture in developing countries, and again, our work examines a more technologically diverse breadth of AGI initiatives. The intended users of the AGI initiatives and the main challenges they seek to address are reflected in the distribution of where implementation has taken place (see Table 1 for the name and summary description of each initiative). Some AGI initiatives specifically targeted women farmers (Tigo Kilimo), farmers with low levels of education (Tigo Kilimo), fishing families (Radio Monsoon) and progressive farmers more open to new technologies and practices (IFFCO Kisan Agriculture App).

Conversely, good reputation and high organizational trust can promote the success of AGI initiatives through user loyalty, sharing of positive experiences and promotion to other farmers (eg, Tigo Kilimo). We reflect on the results and cross-cutting themes discussed above to recommend future pathways to ensure successful smallholder adoption of AGI initiatives for climate change adaptation and mitigation. The observational factors (Table 2) suggest that AGI initiatives driven by demand (from the need for climate adaptation solutions) and opportunities (from the growing population with functional access to the required ICTs) are important.

The use of AGI initiatives could greatly help small farmers to move to climate smart agriculture [101]. for sustainably increasing productivity [44], improving environmental security of life [102] and increasing landscape resilience under a changing climate [103].

Figure 1. Flow diagram for the academic literature search resulting in 11 relevant papers (12 agricultural geographic information (AGI) initiatives) for analysis (see Table 1 for sources)