Nonlinear dynamics in agricultural systems in Chad

Abstract

Changes to agricultural systems in Sub-Saharan Africa are subject to a wide range of drivers, often resulting in rapid and nonlinear dynamics. Such dynamics are however rarely recognized in studies of trends and drivers, which usually rely on assumptions of gradual progressions, and often using highly aggregated data. This paper addresses this lack of recognition and understanding of nonlinear processes in agricultural systems in Sub-Saharan Africa by statistically identifying breakpoints and trends in regional crop production data in Chad for 1983-2016, which also makes it the most extensive and detailed study of the agricultural sector in Chad to date. By summarizing and visualizing the identified breakpoints and trends of a large group of variables in a concise and informative way, the roles of both abrupt and gradual processes are made clear. Results show that the progressions in the data predominantly have been driven by abrupt changes with lasting effects rather than gradual changes, contrary to what analyses on higher levels of aggregation would conclude. The potency of the developed methodology lies in its capacity to identify and summarize nonlinear progressions in agricultural systems constituted by a large set of variables, by separating the influence of abrupt and gradual processes.

Drivers of abrupt and gradual changes in agricultural systems in Chad

“…The patterns of change in regional agricultural production in Chad have been analyzed by Nilsson and Uvo (2018), who found that 83% of the increases in production for the period 1990-2016 can be explained by expansions in harvested area. They also emphasized the importance of distinguishing between abrupt and gradual changes in the agricultural production over this time period, as they found that 80% of the increase in harvested area and 89% of the increase in yield in the Sahelian zone in Chad 1 were due to abrupt changes, i.e., changes occurring at a single year with a statistically detectable lasting effect (see "Agriculture" section).…”

“…Migration is a key feature in demographic change, and is likely to differ considerably between regions, considering different employment opportunities, security threats, and family connections. Nilsson and Uvo (2018) compared growth rates in total and agricultural population to growth in agricultural output for regions in the Sahelian and Soudanian zones in Chad, and found evidence of linear relationships, indicating that both the harvested areas and the yields grow partly in response to population driven demand and labor availability in the respective regions.…”

“…Due to dry growing conditions across the ecological zones in Chad, high ratio of rainfed farming, and varying hydrological conditions, soil water availability is bound to be a key factor behind these interannual variabilities (FEWS NET 2011;Nilsson et al 2019;Republic of Chad 2009). While this warrants an evaluation of the role of soil water availability in long-term changes to agricultural output, the substantial increase in agricultural output seen in all regions in Chad over the past three decades indicate a strong influence of anthropogenic drivers (Nilsson and Uvo 2018).…”

“…The agricultural statistics were acquired from the Chadian government agencies Direction de la Production et des Statistique Agricoles (DPSA) and the Institut National de la Statistique, des Études Économiques et Démographiques (INSEED), and are described in detail in Nilsson and Uvo (2018). They cover the harvested area, yield, and production of cereal crops for 1990-2016 for each administrative region in Chad.…”

“…As the purpose of this paper was to study the drivers of the long-term trends and changes in the agricultural sector, estimates of the trend levels in the agricultural variables for each year were needed. Due to the high interannual variability in the agricultural statistics, as well as the presence of abrupt changes (Nilsson and Uvo 2018), the trend levels were estimated by using the abrupt changes identified in Nilsson and Uvo (2018) combined with 5-year moving averages between points of abrupt changes. These abrupt changes were in Nilsson and Uvo (2018) defined as points in the time series with significant Wald scores, as calculated by the Wald test for structural change in linear regressions (Andrews 1993).…”