Cognitive Biases about Climate Variability in Smallholder Farming Systems in Zambia

Abstract

Given the varying manifestations of climate change over time and the influence of climate perceptions on adaptation, it is important to understand whether farmer perceptions match patterns of environmental change from observational data. We use a combination of social and environmental data to understand farmer perceptions related to rainy season onset. Household surveys were conducted with 1171 farmers across Zambia at the end of the 2015/16 growing season eliciting their perceptions of historic changes in rainy season onset and their heuristics about when rain onset occurs. We compare farmers’ perceptions with satellite-gauge-derived rainfall data from the Climate Hazards Group Infrared Precipitation with Station dataset and hyper-resolution soil moisture estimates from the HydroBlocks land surface model. We find evidence of a cognitive bias, where farmers perceive the rains to be arriving later, although the physical data do not wholly support this. We also find that farmers’ heuristics about rainy season onset influence maize planting dates, a key determinant of maize yield and food security in sub-Saharan Africa. Our findings suggest that policy makers should focus more on current climate variability than future climate change.

Near-term climate change impacts on sub-national malaria transmission

“…A significant difference was observed in the spatial and temporal patterns of malaria over the study period. Similarly, several agriculture based studies [64][65][66][67][68] are consistent with the observed shifts in temperature and rainfall, showing declining rain but increasing temperature trends in southern most areas but the opposite for northern and north-eastern parts of Zambia. Figure 1 shows the posterior probabilities of disease trends assigned to each district, categorised as either having an increasing trend, a constant trend or a decreasing trend.…”

Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields

“…The wet season is generally from October to April and the dry season is from June to September, with the date of rainy season onset earlier in the northern part of the country than in the south. The sowing period extends approximately from October to December, the growing period extends from November to May, and the harvesting season extends from April to June (Waldman et al, 2019). In 2013, Zambia consisted of 72 districts (118 districts in 2020 after the subdivision of some districts), with an average area of 10 450 km 2 and an average agricultural area of 3310 km 2 per district.…”

“Are They Aware, and Why?” Bayesian Analysis of Predictors of Smallholder Farmers’ Awareness of Climate Change and Its Risks to Agriculture

“…The x ik 's are the predictor variables whose definitions are in Table 1 and summarized in Table A1. The varying-intercept model shown in Equation (8) shows that the model intercept would vary by α 0 , while the between-district variation would be captured by the between-intercept stochastic disturbances µ k . Following [55,64,65], we imposed weakly-normal priors on α 0 and β k to ensure proper posterior densities and because these can take either positive or negative values, even though the null hypotheses for these two parameters would be that they are zero.…”

“…We selected these priors to provide little information-which is consistent with [66] who suggests that posterior standard deviations should be smaller than 10% of the corresponding prior standard deviations. We took advantage of modern computationally efficiency of Markov chain simulation techniques-Hamiltonian Monte Carlo (HMC) and its extension the no-U-turn sampler to generate posterior samples of the parameter estimates for Equations (7) and (8). HMC uses "momentum" variables that accelerate each iteration within a parameter space to allow faster mixing and convergence [55,59,60].…”

“…Similarly, [7] argue that understanding people's perception of climate change is a necessary prerequisite for conveying climate change mitigation and adaptation policies. However, empirical research that exists in developing countries is too little to illuminate farmers' awareness of the phenomenon, and how they would want to build resilience of their production systems [8].…”