CLIMATE-CHANGE-INDUCED TEMPORAL VARIATION IN PRECIPITATION INCREASES NITROGEN LOSSES FROM INTENSIVE CROPPING SYSTEMS: ANALYSIS WITH A TOY MODEL

doi:10.15302/J-FASE-2022452

Front. Agr. Sci. Eng.

2022, Vol. 9

Issue (3) : 457-464 https://doi.org/10.15302/J-FASE-2022452

RESEARCH ARTICLE

CLIMATE-CHANGE-INDUCED TEMPORAL VARIATION IN PRECIPITATION INCREASES NITROGEN LOSSES FROM INTENSIVE CROPPING SYSTEMS: ANALYSIS WITH A TOY MODEL

Peter M. VITOUSEK¹(

), Xinping CHEN², Zhenling CUI³, Xuejun LIU³, Pamela A. MATSON⁴, Ivan ORTIZ-MONASTERIO⁵, G. Philip ROBERTSON⁶, Fusuo ZHANG³

¹. Department of Biology, Stanford University, Stanford, CA 94305, USA
². College of Resources and Environment, and Academy of Agricultural Sciences, Southwest University, Chongqing 400716, China
³. College of Resources & Environmental Sciences, China Agricultural University, Beijing 100193, China
⁴. Department of Earth System Science, Stanford University, Stanford, CA 94305, USA
⁵. International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco 56237, Mexico
⁶. W.K. Kellogg Biological Station, and Department of Plant, Soil and Microbial Sciences, Michigan State University, Hickory Corners, MI 49060, USA

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Abstract

● A simple model was used to evaluate how increasing temporal variability in precipitation influences crop yields and nitrogen losses.

● Crop yields are reduced and nitrogen losses are increased at current levels of precipitation variability.

● Increasing temporal variability in precipitation, as is expected (and observed) to occur with anthropogenic climate change will reduce yields and increase nitrogen losses further.

A simple ‘toy’ model of productivity and nitrogen and phosphorus cycling was used to evaluate how the increasing temporal variation in precipitation that is predicted (and observed) to occur as a consequence of greenhouse-gas-induced climate change will affect crop yields and losses of reactive N that can cause environmental damage and affect human health. The model predicted that as temporal variability in precipitation increased it progressively reduced yields and increased losses of reactive N by disrupting the synchrony between N supply and plant N uptake. Also, increases in the temporal variation of precipitation increased the frequency of floods and droughts. Predictions of this model indicate that climate-change-driven increases in temporal variation in precipitation in rainfed agricultural ecosystems will make it difficult to sustain cropping systems that are both high-yielding and have small environmental and human-health footprints.

Keywords crop yield fertilizer timing nitrogen loss precipitation variability toy model

Corresponding Author(s): Peter M. VITOUSEK

About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.

Just Accepted Date: 15 June 2022 Online First Date: 08 July 2022 Issue Date: 09 September 2022

Cite this article:

Peter M. VITOUSEK,Xinping CHEN,Zhenling CUI, et al. CLIMATE-CHANGE-INDUCED TEMPORAL VARIATION IN PRECIPITATION INCREASES NITROGEN LOSSES FROM INTENSIVE CROPPING SYSTEMS: ANALYSIS WITH A TOY MODEL[J]. Front. Agr. Sci. Eng. , 2022, 9(3): 457-464.

URL:

https://academic.hep.com.cn/fase/EN/10.15302/J-FASE-2022452
https://academic.hep.com.cn/fase/EN/Y2022/V9/I3/457

Fig.1 The structure of the toy model. The soil pool is divided into organic forms of C, N and P; biologically available forms of water, N, P and a cation (modeled on calcium and described as M⁺); primary (unweathered) minerals of P and a cation, and secondary (formed in the soil) minerals of P. It includes atmospheric deposition of water, N, P and a cation; losses of inorganic forms of N, P and a cation by leaching, losses of dissolved organic forms of C, N and P by leaching, and gaseous losses of C and N. For the crop, we include uptake of water, N and P from the soil, removals of C, N and P in harvested material, and a flux of C, N and P back to the soil in crop residue.

Fig.2 Effects of the timing of simulated applications of fertilizer (left of x-axis) and of the number of simulated split applications of fertilizer (right of x-axis) on the simulated recovery of N in harvested material (solid line, a proxy for yield) and on losses of reactive N to the environment via leaching and gas fluxes (dashed line). In all cases a consistent 100 and 20 units of N and P were applied, respectively, per growing season, and both recovery and losses of N were averaged over the last 1000 years of simulation. Multiple runs of the model were performed for the November application of fertilizer and for the five-increment application; means and standard deviations are reported for these treatments but standard deviations were small and error bars largely are hidden behind the symbols. For two split applications of fertilizer, application of 40 units of N upon planting in May and 60 units of N in June was used.

Fig.3 Effects of simulated temporal variability in precipitation on the simulated number of floods (solid line) and droughts (dashed line). Both were summed over the last 1000 years of simulation. Simulated temporal variability in precipitation is given as the coefficients of variation of precipitation per month, and (as in Fig. 5) multiple model runs were performed with means and standard deviations calculated and reported at the highest level of variability in precipitation. Floods were defined as occurring when simulated water loss exceeded the water-holding capacity of the upper soil (20 cm of water). Droughts were defined as occurring when two consecutive months received simulated zero precipitation; only droughts that occurred during the crop growing season were included.

Fig.4 Consequences of a management practice designed to offset the effects of greater simulated variability in precipitation. This simulation was performed at the second-highest level of temporal variation in simulated precipitation; it compared the effects of two with five splits in standard fertilizer application both with a total of 100 units of N and 20 units of P. The two applications were simulated to occur in May and July. The height of the bars in each group represent the simulated recovery of N in harvested material, simulated losses of reactive N and the total quantity of N fertilizer applied. Results of these standard treatments were compared with treatments in which we simulated two and five splits of adjusted fertilizer application with lesser and variable amounts of fertilizer applied. Multiple model runs were performed with means and standard deviations calculated and reported for the outputs summarized; standard deviations were small (always < 2% of means) so the error bars are not readily visible.

Fig.5 Effects of simulated temporal variability in precipitation on the simulated recovery of N in harvested material (solid line) and on losses of reactive N to the environment via leaching and gas fluxes (dashed line). Both recovery and losses of N were averaged over the last 1000 years of simulation. In all cases we simulated the consequences of two split applications with 40 units of N applied, with 8 units of P, at planting, and the second application with 60 units of N and 8 units of P in July. Simulated temporal variability in precipitation is given as the coefficients of variation of monthly precipitation, and multiple model runs were performed with means and standard deviations calculated and reported at the highest level of variability in precipitation.

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