Overlaying local and scientific knowledge – climate trend analysis

Focus group concept

There are various definitions of a focus group in the literature. Powell et al. (1996) define a focus group as a group of individuals selected and assembled by researchers to discuss and comment on

the topic of research, from personal experience. Robinson (1999) defines focus group as an in-depth, open-ended group discussion of 1-2 hours’ duration that explore a specific set of issues on a predefined and limited topic. This research method can generate more critical comments than interviews (Watts and Ebbutt, 1987) and the information is expressed in participants’ own words and context without having external interference (Robinson, 1999). Thus, the key characteristic which distinguishes focus groups is the insight and data produced by the interaction between participants. This methodology is very useful to cross-cultural work with ethnic minority groups (Hughes and DuMont, 2002; Naish et al., 1994)

In this section, indigenous knowledge or farmer’s perception is collected by FGD and then evaluated by simple descriptive, algebraic methods and a simplified weighting method based on the Analytic Hierarchy Process (AHP) principal. Perceptions about EWEs, biophysical adaptable capacity of crops to such EWEs and the role of farm enterprises in leveraging income are progressed in five stages: Design and preparation; Farmer recruitment; Implementation;

Transcription; and Data analysis.

Design and preparation

A FGD proposal was developed based on “The Talking Toolkit: How smallholder farmers and local governments can together adapt to climate change” (Simelton et al., 2013). Topics chosen for discussion include: (i) Problem tree, (ii) Village history and hazard timeline, (iii) Calendar farming, (iv) List of exposures to extreme weather events, (v) Ranking suitable trees and crops, and (vi) Ranking livelihood income sources. The first three topics are used to explore background information for further insight discussions related to the last three topics. In discussion topic 6, ranking the importance of farm livelihoods to HH income is designed based on “Decision making with the analytic hierarchy process” in Saaty (2008).

Farmer recruitment

FGDs are conducted in: (i) Bu Cao Village – Suoi Bu Commune (Van Chan, Yen Bai); (ii) Nam Cuom Village–Nam Bung Commune (Van Chan, Yen Bai); (iii) Tay Son Village, Tien Nguyen Commune– (Quang Binh, Ha Giang; and (iv) Group 12, Viet Lam Town – (Vi Xuyen, Ha Giang). The village and commune are purposefully selected to represent two main technologies popularly found for tea plantations in the NMR, conventional and natural organic systems. In this regard, Nam Bung and Viet Lam Town were selected to represent conventional systems while Suoi Bu and Tien Nguyen communes were chosen as representatives for natural organic systems.

In these communes, 37 farmers in total were invited to participate in 4 FGDs with support from Provincial DARD of Ha Giang and Yen Bai; local district and commune authorities. Each FGD was conducted with participation of 8-10 farmers who were conveniently sampled to represent different age and gender groups. List of participants in each FGD is detailed in Appendix A.

Implementation

Each FGD was started with an opening session about the purpose, timing, method of communication and discussion to create the most comfortable atmosphere. Details for each topic and steps carried out in each FGD are presented in Appendix A. The implementation procedure is summarized as follow:

In discussion topic 1 (Problem tree): participants were asked to list three groups of factors that present the most difficulty for agricultural production. Next, in discussion topic 2 (Village history and hazard timeline), farmers recalled the scale and level of impact due to exposure to past hazard events. Crop calendar and cropping systems in the village is discussed in topic 3. Then, exposures to EWEs was listed in discussion topic 4 in order to use them as criteria to rank the level of suitability of trees and crops in discussion topic 5. Topic 6 deals with pair-wise ranking of the importance of livelihood activities (identified in topic 3) to HH’s income.

In this topic, farmers discuss and reveal their perceptions on the criterion – the contribution of farm activity to total income in terms of level and stability. Weight of these criterion are assessed by comparing each pair of farming activities using a score of 1 to 5 (degree of importance). Puts 1/1 if each pair of activities has the same or equal importance, 1/3 if the row activity has stronger importance, and 1/5 if the row activity has extreme importance in terms of level or stability to HH income compared to respective activities in the column (Table 3.1). It is worth noting that the shaded cells in the lower triangular table are left empty since the matrix is symmetric and the diagonal cells are left blank because they are self-comparison.

Table 3.1. A pair-wise comparison matrix of importance of livelihood to HH income livelihood

Cattle husbandry

… … …

Other livestock

… …

Off-farm jobs …

Other

Score for comparison: 1- equal importance 3- stronger importance 5- extremely importance 2, 4 – values for intermediate.

Data analysis

Results from discussion topic 1 to 3 are summarized into different clusters and patterns which serve as a background for evaluating exposures to EWEs in each village. Data outcome from discussion topic 4 and 5 are combined to assess the degree of suitability of tree and crop during exposure to EWEs occurred in the village. This serves for the first layer of adaptation analysis.

Pair-wise comparison data in discussion topic 6 will be processed by AHP excel spreadsheet to derive overall, consistent weightings and rankings of each farm livelihood. Then, these rankings are used to evaluate their contributions to local household income in the second layer of adaptation analysis.

In order to establish the rankings of importance to HH income among livelihoods, synthesize the overall weights and evaluate the consistency of judgments, pair-wise comparison results are processed by AHP excel spreadsheet. This is built following Saaty (2008) and Bunruamkaew (2012).

Step 1. Complete comparison matrix

Enter the weighting scores (as shown in Table 3.1) into the AHP excel worksheet. The lower triangular matrix is filled by computing the pair-wise inputs. If aij is the weighted score of row i column j of the matrix, then the lower diagonal, aji = 1/ aij, is calculated and filled.

Step 2: Normalization of the matrix

Once the matrix is fully filled in Step 1, all numbers in each column are summed. Then, each entry in the column is divided by the column sum to get its normalized value. The sum of all normalized values in each column is 1.

Step 3: Consistency check

Consistency ratio (CR) is calculated to verify the credibility of final judgments obtained by pairwise comparison. If the value of the CR equals 0.1 or less, then the pair-wise comparisons are accepted and reliable. However, if the value is greater than 0.1, meaning that the CRs are indicative of inconsistent judgments, the result is considered unreliable. Procedures and calculations of consistency check are followed (Akalin et al., 2016; Bunruamkaew, 2012).

3.3.1.2. Exploring climatic changing patterns

The aim of this section is to investigate the changing patterns of rainfall and temperature in the four communes where FGDs were conducted and to link these patterns with the occurrence of EWEs e.g. hot spell, cold spell, which have been perceived by local farmers in FGDs.

Temperature and rainfall variables are extracted from ERA-Interim/Operational data from 1989 to 2013.

Temperature

Changing patterns of dekadal average, maximum and minimum temperatures in the four communes are studied through describing statistics. Particularly, statistical mean and coefficient of variation of dekadal maximum, minimum and average temperature are generated and graphed to evaluate their trends in the last 25 years.

Rainfall

The distribution of precipitation throughout the season cycle as well as the total annual rainfall is very important to evaluate its impacts to hydrology, ecology, agriculture and water use (Guhathakurta and Saji, 2013). The historical changing in mean annual precipitation and changing pattern of rainfall are both identified in studied communes by computing mean and seasonality index of rainfall. The relative seasonality of rainfall is represented in the degree of variability in monthly rainfall throughout the year (Adejuwon, 2012; Livada and Asimakopoulos, 2005; Walsh and Lawler, 1981). The seasonality index (SI) supports understanding the rainfall regimes based on the monthly distribution of rainfall. The index is a function of mean monthly and annual rainfall and is computed by the following equation (Walsh and Lawler, 1981):

𝑆𝐼̅ = ¹

𝑅̅ ∑ |𝑋_𝑛− ^𝑅̅

12|

𝑛=12𝑛=1 (17)

Where:

X_n is the mean rainfall of month n R̅ is the mean annual rainfall.

Theoretically, the value of 𝑆𝐼̅ can vary from zero (if all the months have equal rainfall) to 1.83 (if all the rainfall occurs in one month). Table 3.2 shows the different class limits of SI and representative rainfall regimes.

Both temperature and rainfall analysis are performed in STATA 13. The output of 3.3.1.1 and 3.3.1.2 are then matched to verify the credibility of each other.

Table 3.2. Seasonality Index classes and rainfall regimes

Rainfall regime SI class limits

Very equable ≤ 0.19

Equable but with a definite wetter season 0.20–0.39 Rather seasonal with a short drier season 0.40–0.59

Seasonal 0.60–0.79

Markedly seasonal with a long drier season 0.80–0.99

Most rain in 3 months or less 1.00–1.19

Extreme, almost all rain in 1–2 months ≥ 1.20 Source: Walsh and Lawler (1981)