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 CO
Being an extensively produced natural fiber on earth, cotton (
However, the growth of cotton plants differs at varying stages of plant development and by plant organ
Crop models have been used to assess the effect of changing climate conditions on crop productivity, but the main focus has been on major staple crops – such as maize, wheat, rice, and soybean
The response of other crops has been assessed less thoroughly, despite their importance for economies or human nutrition.
With this study, we aim to examine the impacts of climate change on cotton productivity in different regions across the world.
We therefore add cotton as an additional crop to the global dynamic vegetation, hydrology, and crop growth model LPJmL (Lund-Potsdam-Jena managed land) version 4.0
The global dynamic vegetation model LPJmL is a well-established and thoroughly evaluated model
Twelve crop types are already implemented in LPJmL (temperate and tropical cereals, pulses, maize, rice, temperate and tropical roots, sunflower, soybeans, groundnut, rapeseed, sugar cane)
Key parameters of cotton according to
Values marked with an asterisk (
Wild cotton is a deciduous perennial tree, and the fruiting habit of the plant is not clearly established; i.e., vegetative and reproductive growth occur at the same time
To account for the current production system, cotton is implemented in LPJmL as small agricultural trees that are planted annually and removed at the end of the growing period, representing the annual production mode.
The phenology – i.e., the temporal dynamic of the canopy greenness – is computed with the growing season index concept as described and parameterized by
A possible simultaneous establishment of herbaceous PFTs in the same areas of agricultural trees, representing grasses and weeds (for modeling details see
A country-specific planting density (
The growing season of cotton plants is prescribed from the sources specified in Sect.
Five hydrologically and thermally active layers represent the soil profile in LPJmL where roots access water, depending on their PFT/CFT-specific root distribution
Cotton is produced in rainfed or irrigated systems, whereas irrigation generally serves to reduce the impacts of rainfall deficits and thus reduces interannual yield variability.
However, the actual amount of water applied to fields is unknown and determined by water availability, water management systems, and economic rationale.
The extent to which rainfall deficits are compensated for by irrigation is thus not only a question of equipment for irrigation
If the soil water content in the upper 50 cm of the soil falls below 90 % of field capacity, an irrigation event is triggered. Soil water lower than atmospheric water demand requires a daily net irrigation (NIR, millimeters). NIR is calculated as the amount of water needed to fill soil water up to field capacity
Inefficiencies of different irrigation systems cause additional water needs to meet crop water demand.
For that reason, LPJmL considers conveyance efficiency (
This model development is based on LPJmL4, a model version that does not account for nutrient limitations, and, thus, fertilizer effects are not considered.
All simulations are conducted at a 0.5
Sowing dates and growing period were provided as gridded model input, combining sowing and harvest information provided by the ICAC's World Cotton Calendar (WCC)
Cotton yield levels modeled under different irrigation options on irrigated cotton areas (Sect.
In order to evaluate the performance of this extended LPJmL model version, simulated historical cotton yields are compared to observed data (Fig.
Comparing simulated cotton yields [t ha
For these simulations, the planting densities in LPJmL have not been calibrated against observed yield levels but are based on reported planting densities (Fig. S1). The model simulations can reproduce statistically significant shares of reported variability in time of intensely managed top producing countries, such as the USA and Australia as well as a few West African countries (Figs.
Comparing interannual yield variability for the top 10 cotton-producing countries. Numbers in each plot depict the correlation coefficient between simulated residuals and FAOSTAT residual data.
The different irrigation options (on irrigated cropland only) are shown in colored lines.
The color of the correlation coefficient indicates the best-fitting irrigation option.
For Turkmenistan, yields have only been reported from 1992 onward
Time series of historical global cotton production [million tonnes per year]. The number in the plot depicts the correlation coefficient between simulated residuals and FAOSTAT residuals.
We further evaluate the model results with respect to the water consumption of cotton production against data provided by
Virtual water content and consumptive water use for cotton production in the major cotton-producing countries for the period 1997–2001. Reference data taken from
For the evaluation of the modeled cotton yield response to elevated [CO
Considering the overall effect of climate change and CO
Simulated global cotton production [million tonnes] for different RCPs. Transparent colors show the uncertainty ranges of five different GCM patterns.
Spatial patterns of projected changes in cotton yields (Fig. S10) show that increases are mainly expected in cooler or irrigated environments (Fig. S2), but exceptions exist, such as Pakistan and northern India, where cotton yields are projected to only slightly increase despite irrigation. Overall, the spatial patterns of projected yield increases seem to be quite static but scale with the emission scenario (RCP, Fig. S10).
The projected increases in yield are the result of interacting processes, which are often dominated by a positive response to elevated [CO
Without the beneficial effect of elevated [CO
While changes in atmospheric CO
Simulated changes in virtual water content of seed cotton [m
While the virtual water content improves by the CO
The model can reproduce national yield levels very well (Fig.
The temporal variation in cotton yields can only partly be reproduced.
This comparison is hampered by using static management assumptions in the absence of good spatially and temporally resolved management data, which is a general difficulty in evaluating gridded crop models' performance
The good agreement with
Elevated [CO
Cotton is a perennial, indeterminate crop, and cultivated species are generally photoperiodic insensitive. Consequently, warmer temperatures will increase the rate of plant development but not necessarily reduce the length of growing season if temperature seasonality is the limiting factor
Climate change is associated with changes in patterns of precipitation and water availability; hence, cotton plants in some regions may be subjected to plant water deficits.
Water deficit limits growth and productivity of cotton plants, and the severity of the problem may increase due to changing world climatic trends
Our results suggest that the beneficial effects of elevated [CO
Overall, our simulation of climate change impacts on global cotton production results in patterns similar to other crops.
Given the economic relevance of cotton production in areas such as West Africa or South Asia, climate change (elevated temperature and water stress effects) poses additional stress and deserves special attention.
This holds particularly true as agriculture in these regions is already under pressure from increased demand for intensification considering rapid population growth.
Changes in virtual water content and water demands for cotton production are of special importance, as cotton production is known for its intense water consumption that led, e.g., to the loss of most of the Aral Sea
The implications of climate impacts on cotton production on the one hand and the impact of cotton production on water resources (with major impacts particularly in India and Uzbekistan) on the other hand illustrate the need to assess how future climate change may affect cotton production and its resource requirements.
The inclusion of cotton in LPJmL allows for various large-scale studies to assess impacts of climate change on hydrological factors and its implications for agricultural production and carbon sequestration.The limited availability of data (such as valid information on tree density, irrigation management, and sowing dates) substantially limits model performance and evaluation.
Another issue related to data scarcity is the need for scenarios of future cropping patterns, adaptation, and management as a consequence of climatic and socioeconomic change.
With climate change very likely affecting the potential growing areas of cotton (as for other agricultural crops) and their profitability, it is essential to provide crop yield estimates and associated water requirements under different climate scenarios to other research projects, e.g., on land-use change projections
As the most widely produced natural fiber, cotton is of high importance to economies, but the growth of and irrigation water demand on cotton may be challenged by future climate change.
To study how future cotton productivity is affected by projected climate change, we use the global biogeochemical model of hydrology, carbon exchange, and crop growth, LPJmL, expanded to include cotton plants.
Available data on observations and published estimates are used to validate the model, and a set of climate scenarios following the ISIMIP protocol are used to simulate global future cotton yield and water consumption. We then analyze the global cotton production and irrigation water consumption under spatially varying present and future climatic conditions.
Our results suggest that the beneficial effects of elevated [CO
The model code of LPJmL4 is publicly available through PIK's GitHub repository at
The supplement related to this article is available online at:
YJ and CM designed the study. YJ and WvB developed the model code, and YJ performed the simulations. YJ prepared the manuscript with contributions from all co-authors.
The authors declare that they have no conflict of interest.
We thank Jens Heinke and Susanne Rolinski for valuable discussions.
This research has been supported by the Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit (grant no. 16_II_148_Global_A_IMPACT).The publication of this article was funded by the Open Access Fund of the Leibniz Association.
This paper was edited by Pieter van der Zaag and reviewed by Kokou Adambounou Amouzou and one anonymous referee.