Adaptation, mitigation and food security: Multi-criteria ranking system for climate-smart agriculture technologies illustrated for rainfed rice in Laos

Building the resilience of smallholder farmers to climate variability: Using climate-smart agriculture in Bono East Region, Ghana

The concept of climate-smart agriculture (CSA) emerges from a requirement to come up with advanced solutions towards the intricate and combined objectives of enhancing crop yields, ameliorating resilience, and encouraging a low-emissions agricultural sector. This study examines how smallholder farmers are building their resilience to climate variability using CSA practices in the Bono East Region, Ghana. Specifically, the study sought to: (i) assess the trends of temperature and rainfall for the period 2011 to 2021; (ii) identify and rank CSA practices used by the smallholder farmers for resilience building in agricultural systems; and; (iii) determine the barriers militating against smallholder farmers' implementation of the prioritized CSA practices. Standardized rainfall and temperature anomalies integrated with Sen's slope were used to determine the temperature and rainfall trends. One hundred and fifty random household surveys in five selected communities (Benkai, Fiaso, Traa, Awurano, and Bomini) accompanied by five key informant interviews were used to collect field data. The CSA practices identified by the farmers and the barriers opposing the implementation of these practices were ranked using the Relative Importance Index (RII) and Weighted Average Index (WAI) respectively. Results showed that rainfall was inconsistent and temperature rose from 2011 to 2021 in the study area. Results also revealed that the key CSA practices implemented by the farmers were appropriate fertilizer application (RII = 0.758), mixed farming (RII = 0.735), and crop diversification (RII = 0.717). However, in the implementation of these CSA practices, the farmers were confronted with key barriers including increased occurrences of diseases and pests (WAI = 1.173), restricted access to agricultural technologies (WAI = 1.100), and excessive price of improved crop varieties (WAI = 1.067). The study concludes that the resilience of smallholder farmers in Ghana can be built through the effective implementation of the aforementioned CSA practices.

The Progress of the Development of a Climate-smart Agriculture in Europe: Is there Cohesion in the European Union?

The development of climate-smart agriculture (CSA) is crucial in ensuring the creation of a low-carbon society and mitigation of climate change. These tasks require concerted actions from multiple stakeholders since the very concept of CSA is rather complex and requires multi-dimensional consideration. This study defines and applies various indicators to evaluate the development of CSA in the European Union (EU). To do this, three different multi-criteria decision-making methods, namely Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR; multi-criteria optimization and compromise solution), were employed for the construction of a composite indicator. A combination of both objective (entropy) and subjective (Analytic Hierarchy Process) weighting techniques was utilized to derive the weights of the indicators. The leaders in the EU in terms of CSA are Austria, Denmark and the Netherlands, whereas the countries with the lowest levels of CSA development are Cyprus, Greece and Portugal. This study also revealed divergence in the development of these practices in the EU-24 for the period 2004-2019. Thus, a more inclusive approach is needed to ensure the spread of climate-smart ideas in European agriculture sectors.

Rice Management Decisions Using Process-Based Models With Climate-Smart Indicators

Irrigation strategies are keys to fostering sustainable and climate-resilient rice production by increasing efficiency, building resilience and reducing Greenhouse Gas (GHG) emissions. These strategies are aligned with the Climate-Smart Agriculture (CSA) principles, which aim to maximize productivity whilst adapting to and mitigating climate change. Achieve such mitigation, adaptation, and productivity goals- to the extent possible- is described as climate smartness. Measuring climate smartness is challenging, with recent progress focusing on the use of agronomic indicators in a limited range of contexts. One way to broaden the ability to measure climate-smartness is to use modeling tools, expanding the scope of climate smartness assessments. Accordingly, and as a proof-of-concept, this study uses modeling tools with CSA indicators (i.e., Greenhouse Intensity and Water Productivity) to quantify the climate-smartness of irrigation management in rice and to assess sensitivity to climate. We focus on a field experiment that assessed four irrigation strategies in tropical conditions, Continuous Flooding (CF), Intermittent Irrigation (II), Intermittent Irrigation until Flowering (IIF), and Continuous soil saturation (CSS). The DNDC model was used to simulate rice yields, GHG emissions and water inputs. We used model outputs to calculate a previously developed Climate-Smartness Index (CSI) based on water productivity and greenhouse gas intensity, which score on a scale between−1 (lack of climate-smartness) to 1 (high climate smartness) the climate-smartness of irrigation strategies. The CSS exhibited the highest simulation-based CSI, and CF showed the lowest. A sensitivity analysis served to explore the impacts of climate on CSI. While higher temperatures reduced CSI, rainfall mostly showed no signal. The climate smartness decreasing in warmer temperatures was associated with increased GHG emissions and, to some extent, a reduction in Water Productivity (WP). Overall, CSI varied with the climate-management interaction, demonstrating that climate variability can influence the performance of CSA practices. We conclude that combining models with climate-smart indicators can broaden the CSA-based evidence and provide reproducible research findings. The methodological approach used in this study can be useful to fill gaps in observational evidence of climate-smartness and project the impact of future climates in regions where calibrated crop models perform well.

Farmers’ Participatory Alternate Wetting and Drying Irrigation Method Reduces Greenhouse Gas Emission and Improves Water Productivity and Paddy Yield in Bangladesh

In dry season paddy farming, the alternate wetting and drying (AWD) irrigation has the potential to improve water productivity and paddy production and decrease greenhouse gas (GHG), such as methane (CH4) and nitrous oxide (N2O), emissions when compared to continuous flooding (CF). Participatory on-farm trials were conducted from November 2017 to April 2018 in the Feni and Chattogram districts of Bangladesh. Total 62 farmers at Feni and 43 at Chattogram district, each location has 10 hectares of land involved in this study. We compared irrigation water and cost reductions, paddy yield, and CH4 and N2O emissions from paddy fields irrigated under AWD and CF irrigation methods. The mean results of randomly selected 30 farmers from each location showed that relative to the CF irrigation method, the AWD method reduced seasonal CH4 emissions by 47% per hectare and CH4 emission factor by 88% per hectare per day. Moreover, the AWD decreased the overall global warming potential and the intensity of GHG by 41%. At the same time, no noticeable difference in N2O emission between the two methods was observed. On the other hand, AWD method increased paddy productivity by 3% while reducing irrigation water consumption by 27% and associated costs by 24%. Ultimately it improved water productivity by 32% over the CF method.

Spatiotemporal Evolution of the Environmental Adaptability Efficiency of the Agricultural System in China

Since its emergence, the development of agriculture has always been closely related to changes in the natural environment. The productivity and development of agriculture largely depend on natural conditions and agriculture and has an important impact on the environment. The development of modern conventional agriculture has also led to a series of ecological, economic, and social problems that threaten human development and sustenance. China has historically been heavily reliant on agriculture and provides food and clothing for approximately 22% of the world’s population while only accounting for 9% of the world’s cultivated land and 6% of freshwater resources. Since the 21st century, the agricultural development of China has faced increasing resource and environmental constraints due to rapid industrialization and urbanization. Based on the perspective of efficiency evolution, data envelopment analysis (DEA) and spatial autocorrelation analysis (SAA) were used to test the environment adaptability efficiency within China’s agricultural systems across 30 provinces, autonomous regions, and municipalities, and explore its temporal and spatial evolution patterns and characteristics. Our study thus possesses both theoretical and practical significance. Furthermore, this study would enable the development of methods to assess China’s agricultural systems, in addition to providing a theoretical basis and guidelines for the creation of sustainable agriculture development strategies both in China and in other countries and regions. The following are the main conclusions of this study: (1) from 2000 to 2018, the overall environmental adaptability efficiency within China’s agricultural systems exhibited a gradual upward trend, achieving a transition from medium-level efficiency towards high-level efficiency, and the environmental adaptability of agricultural systems continued to increase. However, a certain gap remained between the level achieved and the DEA’s level of effectiveness, and therefore additional efforts are required to close this gap. (2) The environmental adaptability efficiency within China’s agricultural system showed a significant positive correlation in spatial distribution. Particularly, clear spatial aggregation characteristics were observed at the provincial level, which was also characterized by strong features of spatial dependence and spatial heterogeneity. Moreover, the degree of spatial aggregation increased gradually over time. High-value areas were mainly located along the southeast coastal area, whereas low-value areas were primarily located in the inland areas of the northwest. Therefore, environmental adaptability efficiency generally followed a northwest-southeast spatial distribution.

Does the adaptation of climate-smart agricultural practices increase farmers' resilience to climate change?

Traditional agricultural practices, extensive use of inputs, and abrupt changes in climate have been of great concern to agriculture production around the world, especially in developing countries. Therefore, it is very vital to adopt and expand Climate-Smart agricultural (CSA) practices. By the cross-sectional data of 350 cotton farmers from major cotton-growing districts of Punjab Pakistan, adoption of CSA practices such as irrigation and soil and crop management practices is evaluated, and factors which affect farmer adoption decision and its impact on poverty, income, and yield are estimated by using logistic regression and propensity score matching (PSM) respectively. The results found that education, access to credit, tubewell ownership, farming experience, and access to extension services positively influenced farmers' adoption behavior. Further, PSM results revealed that adoption of CSA practices is economical, financially, environmentally desirable, and pro-poor. According to these findings, ultimately adoption would help in reducing the negative impact of climate change on the cotton crop by ensuring profits, removing the barriers in the adoption, disseminating the information about CSA, and strictly enforcing the regulations for CSA.