Climate change impacts on regional rice production in China

Lv, Zunfu; Zhu, Yan; Liu, Xiaojun; Ye, Hongbao; Tian, Yongchao; Li, Feifei

doi:10.1007/s10584-018-2151-0

Cited by 75 publications

(38 citation statements)

References 43 publications

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“…In conclusion, this empirical work highlights the vulnerability of agriculture to climate change, particularly in Africa. The impacts of climate change on cereal production would be particularly pronounced in Sahelian countries [24].…”

Section: Empirical Reviewmentioning

confidence: 99%

Impact of Climate Change on Cereal Yield and Production in the Sahel: Case of Burkina Faso

Sossou

Igue

Diallo

2020

AJAEES

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Climate change is one of the biggest challenges of the 21st century. It affects all countries in the world, especially Sahelian countries in Africa. This paper aimed at evaluating the impact of climate change on cereal yield in Burkina Faso. The ordinary least squares (OLS) was applied to time-series data from 1991 to 2016 collected on the World Bank website. The results have shown that temperature adversely affects yield and cereal production, while precipitation has positive effect. An increase in rainfall of 1 millimetre would increase cereal production by 385 tons in the long term and 252 tons in the short term. In the same, an increase in rainfall of 1 millimetre would increase agricultural yield by 9 kg per hectare in the long term. However, in the short term, an increase in temperatures of 1ºC would result in a decrease in cereal production and agricultural yield of 134748 tons and 72 kg per hectare, respectively. However, in the long term, a rise in temperatures of 1ºC would result in a decrease in cereal production and cereal yield of 154 634 tons and 1074 kg per hectare, respectively. Besides, the results indicate that the emission of carbon dioxide (CO2) has no significant effect on yield and cereal production. Implementing effective adaptation strategies, such as access to improved seed, introduce smart agriculture in the system of cereal in Burkina Faso and increasing irrigation infrastructure could reduce the cereal production's vulnerability to climate shocks.

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Section: Empirical Reviewmentioning

confidence: 99%

Impact of Climate Change on Cereal Yield and Production in the Sahel: Case of Burkina Faso

Sossou

Igue

Diallo

2020

AJAEES

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“…T A B L E 3 Statistical tests of the differences between the APSIM outputs resulting from observed and interpolated climate variables and the differences between the interpolated spatial APSIM-output maps resulting from the observed climate variables, interpolated climate variables and the 5-km gridded climate variables An obvious rationale for using site observed data as inputs to agricultural models is that such models were built to simulate the processes of crop systems at point scale with many specific functions sensitive to the observed climate and weather extreme values. However, the scattered nature of observed data are often considered as inadequate for high resolution spatial modelling, so use of gridded data input have become a preferred choice for researchers (Priya and Shibasaki, 2001;Lv et al, 2013;Yu et al, 2014;Lv et al, 2018). Our study suggests that this approach can cause large errors in the results of spatial modelling.…”

Section: Discussionmentioning

confidence: 89%

“…An obvious rationale for using site observed data as inputs to agricultural models is that such models were built to simulate the processes of crop systems at point scale with many specific functions sensitive to the observed climate and weather extreme values. However, the scattered nature of observed data are often considered as inadequate for high resolution spatial modelling, so use of gridded data input have become a preferred choice for researchers (Priya and Shibasaki, ; Lv et al ., ; Yu et al ., ; Lv et al ., ). Our study suggests that this approach can cause large errors in the results of spatial modelling.…”

Section: Discussionmentioning

confidence: 99%

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The implication of spatial interpolated climate data on biophysical modelling in agricultural systems

Liu

Wang³

et al. 2019

Intl Journal of Climatology

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Spatial modelling of agricultural production has been more frequently analysed to assist with regional and long‐term planning. Typically, point‐scale crop models are used to construct these analyses with researchers using various sources of input data, including observed and interpolated gridded climate data. Understanding the implication of data choice on production estimates is crucial to appreciate the consequences of selecting methodological approaches for agricultural model outputs. In this study, we compared site observed climate data and gridded data sets interpolated from site observations that are commonly used the Australian climate data set. We assessed the consequences of the differences in climate variables (biases) to the modelled outputs. Our results showed that the major differences between gridded and observed climate data sets were for rainfall variables. Interpolated gridded data tended to have larger rainfall frequency and smaller rainfall intensity, leading to lower surface runoff and higher soil evaporation that caused less plant water uses and less nitrogen uptake, and ultimately resulted in biases in simulated crop biomass and yield. In addition, the results indicated that agricultural models implemented with gridded data could produce similar overall model outputs as those driven by observed climate data, but can result in more uncertainties in simulated spatial outputs. This is particularly evident in regions with high rainfall. Our results showed that applying agricultural models with observed data and then interpolating these results spatially might be the optimal approach to minimize the biases in production modelling outputs and reduce computing time and storage.

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“…During the anthesis stage of single‐season rice, which occurs from July to August and is the most sensitive stage to high‐temperature stress (Jagadish et al, 2010; Zhou et al, 2014), heat stress reduces yields by 10 to 20% for indica rice and 30 to 40% for japonica rice. As the frequency of high‐temperature events increases, their effects on rice yield and food security have become a grave concern, especially for japonica rice (Espe et al, 2017; Lv et al, 2018). Rice sowing date adjustment is an effective method of avoiding heat stress during anthesis stage (Raju et al, 2013).…”

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confidence: 99%

Sowing Dates have Different Effects on Spikelet Formation among Different Photoperiod‐Sensitive Rice Genotypes

Wang

Chen

et al. 2019

Agronomy Journal

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The photoperiod sensitivity of rice (Oryza sativa L.) cultivars plays an important role in rice growth and yield formation. To ascertain the different effects of sowing dates on spikelet formation for different photoperiod‐sensitive genotypes, 10 sowing dates were selected to assess differences in spikelet differentiation and degeneration for photoperiod‐sensitive cultivars yongyou 538 (YY538) and xiushui134 (XS134) and photoperiod‐insensitive cultivar chujing27 (CJ27). The yield of the cultivars differed significantly among different sowing dates and was primarily influenced by the spikelet number per panicle. With different sowing dates, shorter daylengths, and increasing temperatures significantly shortened the duration from sowing to heading for YY538 and XS134, while that of CJ27 was less changed and influenced by the average temperature. Rice growth duration was the primary factor limiting dry matter accumulation (DMA), which was correlated with the number of differentiated spikelets (NDS) and the proportion of degenerated spikelets (PGS) in photoperiod‐sensitive varieties. High temperature weather with a relatively large temperature gap and strong solar radiation promoted spikelet differentiation in all three cultivars. Extremely high and low temperatures aggravated spikelet degeneration, but YY538 and XS134 were not exposed to low temperatures because their duration of growth was shortened to a greater extent than that of CJ27 with late sowing dates. Under different sowing dates, the NDS and PGS were evaluated by partial least squares regression (PLSR) with DMA, rice growth duration, temperature, and solar radiation. These results provide a reference for sowing date adjustment with climate changes in different regions. Core Ideas Rice yield with different sowing dates was determined by spikelet number. Rice growth duration was the primary factor affecting dry matter accumulation. High temperature and more dry weight increased spikelet differentiation. Sowing photoperiod‐sensitive cultivars at a late date avoided chilling injury. Spikelet formation can be predicted by partial least squares regression with sowing dates.

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Climate change impacts on regional rice production in China

Cited by 75 publications

References 43 publications

Impact of Climate Change on Cereal Yield and Production in the Sahel: Case of Burkina Faso

Impact of Climate Change on Cereal Yield and Production in the Sahel: Case of Burkina Faso

The implication of spatial interpolated climate data on biophysical modelling in agricultural systems

Sowing Dates have Different Effects on Spikelet Formation among Different Photoperiod‐Sensitive Rice Genotypes

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