Global cotton production under climate change – Implications for yield and water consumption

Abstract

Abstract. Being an extensively produced natural fiber on earth, cotton is of importance for economies. Although the plant is broadly adapted to varying environments, the growth of and irrigation water demand on cotton may be challenged by future climate change. To study the impacts of climate change on cotton productivity in different regions across the world and the irrigation water requirements related to it, we use the process-based, spatially detailed biosphere and hydrology model LPJmL (Lund–Potsdam–Jena managed land). We find our modeled cotton yield levels in good agreement with reported values and simulated water consumption of cotton production similar to published estimates. Following the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) protocol, we employ an ensemble of five general circulation models under four representative concentration pathways (RCPs) for the 2011–2099 period to simulate future cotton yields. We find that irrigated cotton production does not suffer from climate change if CO2 effects are considered, whereas rainfed production is more sensitive to varying climate conditions. Considering the overall effect of a changing climate and CO2 fertilization, cotton production on current cropland steadily increases for most of the RCPs. Starting from ∼65 million tonnes in 2010, cotton production for RCP4.5 and RCP6.0 equates to 83 and 92 million tonnes at the end of the century, respectively. Under RCP8.5, simulated global cotton production rises by more than 50 % by 2099. Taking only climate change into account, projected cotton production considerably shrinks in most scenarios, by up to one-third or 43 million tonnes under RCP8.5. The simulation of future virtual water content (VWC) of cotton grown under elevated CO2 results for all scenarios in less VWC compared to ambient CO2 conditions. Under RCP6.0 and RCP8.5, VWC is notably decreased by more than 2000 m3 t−1 in areas where cotton is produced under purely rainfed conditions. By 2040, the average global VWC for cotton declines in all scenarios from currently 3300 to 3000 m3 t−1, and reduction continues by up to 30 % in 2100 under RCP8.5. While the VWC decreases by the CO2 effect, elevated temperature acts in the opposite direction. Ignoring beneficial CO2 effects, global VWC of cotton would increase for all RCPs except RCP2.6, reaching more than 5000 m3 t−1 by the end of the simulation period under RCP8.5. Given the economic relevance of cotton production, climate change poses an additional stress and deserves special attention. Changes in VWC and water demands for cotton production are of special importance, as cotton production is known for its intense water consumption. The implications of climate impacts on cotton production on the one hand and the impact of cotton production on water resources on the other hand illustrate the need to assess how future climate change may affect cotton production and its resource requirements. Our results should be regarded as optimistic, because of high uncertainty with respect to CO2 fertilization and the lack of implementing processes of boll abscission under heat stress. Still, the inclusion of cotton in LPJmL allows for various large-scale studies to assess impacts of climate change on hydrological factors and the implications for agricultural production and carbon sequestration.

Impacts of climate change on water resources in the major countries along the Belt and Road

“…These two processes are roughly cancelled out. Indeed, a recent study found that climate change barely increased crop water demand considering both the temperature and CO 2 effects (Jans et al, 2021). We acknowledge that the temperature-CO 2 interactions on plant growth and water use are complex and may vary in different climates and cropping systems.…”

“…We ignored the change of irrigation rate per unit cropland area caused by climate change due to insufficient knowledge on this complex issue. Many studies have shown that increasing atmospheric CO 2 concentration will increase plant water use efficiency and thus reduce agricultural water demand (Alkama, Kageyama & Ramstein, 2010;Rajagopalan et al, 2018;Jans et al, 2021). Whereas, studies also have shown that increasing temperature will increase cropland water use due to the increase of evapotranspiration (Mahmood et al, 2006;Wada et al, 2013;Sylla et al, 2018).…”

“…The net effects of these two competing processes are not conclusive. However, most studies have shown that these two processes are on the same level, indicating that the net effect on water demand is minor (Lee & Huang, 2014;Jans et al, 2021).…”

“…Crop water demand under future climate change is mainly determined by two competing processes: rising temperature stimulating ET and elevated CO 2 concentration improving water use efficiency. So far, experimental and model simulation studies have shown that warming alone may increase water demands by 3.6-15% through the boost of ET (Wang et al, 2016;Sylla et al, 2018;Jans et al, 2021) and meanwhile, the CO 2 effect alone will decrease water consumption by 4.1-12.2% through CO 2 -induced decrease of stomatal conductance (Yano, Aydin & Haraguchi, 2007;Xu et al, 2016;Nechifor & Winning, 2019). These two processes are roughly cancelled out.…”

Data-Driven Evaluation and Optimization of Agricultural Sustainable Development Capability: A Case Study of Northern Anhui

“…The intention is to focus on the development and innovation of digital agriculture [1,2], promote the optimal allocation of agricultural resources [3], accelerate the optimization of agricultural industrial structures [4], improve the efficiency and competitiveness of agricultural production and promote its sustainable development. Nevertheless, the increase in world population [5], shortage of water resources [6,7], and environmental pollution [8,9], are creating new challenges for the sustainable development of world agriculture [10]. Therefore, it is Processes 2021, 9, 2036 2 of 18 necessary to promote a sustainable agricultural model [11], build evaluation indicators based on these challenges, and objectively measure the capacity of regional sustainable agricultural development by using data to optimize its capability.…”