Benefits Comparison Analysis of Different Rice and Wheat Cropping Patterns to Adapt to Climate Change

Abstract

Based on the input-output survey of farmers and experts in one of the Jiangsu GEF project areas, the Cost-Benefit analysis method and greenhouse gas estimation method recommended by IPCC were applied to evaluate and compare the social, economic and ecological benefits of artificial transplanting (ATR), mechanical transplanting (MTR) and direct seeding (DSR) rice under wheat-rice Double Late mode (late rice harvest and late wheat sowing). Results showed that the MTR and DSR rice achieved obvious social benefits. Farming measures resulted in excessive emission of anthropogenic greenhouse gases. Through the use of ATR rice and wheat rotation mode it is possible to obtain most economic and ecological benefits. The Double Late mode of action had good application prospects, but the key to implementation was the timely exploitation of the recently increased availability of agricultural climate resources. The cropping pattern of combining the wheat-rice Double Late mode with the ATR was a better choice in mitigating and adapting to climate change.

Keywords

climate change ; adaptation ; yield ; benefit ; greenhouse gas emissions

1. Introduction

Agriculture is one of the areas which are most sensitive to climate change, as well as the industry which is most reliant on climate. Adopting active adaptation measures could mitigate or eliminate the potential damage caused by climate change, promote sustainable development in agriculture, ensure food supply and the national economic development, and increase farmers’ income [ Jiang, 2008 ; ECSCNARCC (Editorial Committee for Second China’s National Assessment Report on Climate Change), 2011  ;  Song et al ., 2012 ]. In recent years, China’s agricultural sector has established a series of policies on agriculture to adapt to climate change, and summarized abundant practice experience to develop corresponding adaptation technology [ CNACDO (China’s National Agricultural Comprehensive Development Office), 2010  ;  Ju et al ., 2011 ]. Peasants have also taken adaptation actions actively or passively [ Lü and Chen , 2010 ]. Analyzing and evaluating the integrated benefits of adaptation measures is significant in carrying out correct and prompt adaptation and playing the seeking advantages and avoiding disadvantages role of adaptation technology adequately for government and farmers.

China’s National Agricultural Comprehensive Development Office utilized the grants provided by Global Environment Facility (GEF) to extend the adaptation measure of wheat-rice rotation and multiple cropping Double Late mode of action (hereinafter referred to as wheat-rice Double Late mode) in one of the Jiangsu GEF project areas. To what extent did this mode of action benefit from climate factors? What was the benefit likely to be? How substantial was the potential likely to be? All of these were valuable research areas. Most recent researches focused on one or several crops’ production cost and benefit, or compared the economic effect of using different cropping measures on one crop [ Zhu et al ., 2011  ; Chen, 2009 ; Chen and Chen, 2011  ;  Wen et al ., 2007 ]. Few of them were based on the ideas and concepts of climate change adaptation, or researched the rotation system in a region through quantitive data over a period of years, especially on the comprehensive benefits of rice planting methods and wheat-rice Double Late mode in the southern part of China’s Huang-Huai-Hai Plain. Based on the input-output questionnaire survey of farmers and experts in one of the Jiangsu GEF project areas, the Cost-Benefit analysis method and greenhouse gas (GHG) estimation method recommended by IPCC were applied to compare the social, economic and ecological benefits of three kinds of rice planting methods, including artificial transplanting (ATR) of strong seedlings, mechanical transplanting (MTR) of weak seedlings and direct seeding (DSR), and wheat-rice Double Late mode to evaluate their applications’ potential to adapt to climate change.

2. Material and methodology

2.1. Study area

The impacts of climate change were also observed, from a global warming perspective, in Jiangsu GEF project areas. The increasing rate of temperature was 0.26°C per decade from 1961 to 2008 and has risen to 0.58°C per decade since the 1990s. Annual precipitation fluctuated with an increasing trend of 2.9 mm per decade [ Zhang et al. , 2011 ]. The agricultural climate resources measured by agricultural demarcation temperature changed significantly as well. In the case of Xuzhou, accumulated temperature of above 0°C and 10°C increased by 148.9 and 146.0°C d per decade respectively, while days of above 0°C and 10°C increased by 2.2 and 4.3 d per decade respectively from 1980 to 2011 (Fig. 1 ).

Figure 1. Active accumulated temperature of above 0°C and 10°C in Xuzhou from 1980 to 2011 (dashed line: linear trend; data from http://cdc.cma.gov.cn/home.do )

Wheat-rice rotation and multiple cropping was the primary planting pattern in northern Jiangsu. Due to the recent rise in labor cost, farmers expanded the planting scale of ATR and DSR to save labor and guarantee self-supporting food production, resulting in the coexistent situation of ATR, MTR and DSR, while the wheat was still cultivated using the traditional method of direct seeding.

Xinyi, one of the key GEF counties in Xuzhou, Jiangsu province, was the survey area in the study. Most rice was cultivated using artificial transplanting and mechanical transplanting in Xinyi, while the use of direct seeding was scarce. Compared with ATR, MTR and DSR were harvested late, and following the increased popularity of the GEF project, the sowing date of winter wheat was postponed from late September to early and mid- October, leading to wheat-rice Double Late mode (late rice harvest and late wheat sowing).

2.2. Survey method and content

After confirming the background of the GEF project area and the character of adaptation technology through literature’s overview, and informal discussion with officials and experts, three sets of questionnaires on the rice and wheat harvested in 2010 and 2011 were designed and revised for the households, village committee and agricultural materials shops. Households were randomly selected to complete the one-to-one question-and-answer survey and the number of questionnaires for each household group, including ATR, MTR and DSR, was guaranteed to be no less than 15. Furthermore, officials from the village committee and owners of agricultural materials shops were invited to finish the group survey. Questionnaires for agricultural materials shops contained much information including variety, volume, and price of fertilizers and pesticides. Questionnaires for village committees covered the price of local labor, direct food subsidies, selective seed subsidies and other public income and expenditure. Questionnaires for households consisted of questions about basic situation, crops’ growth stages, yield and gains, and the input costs which comprised seeds, fertilizer, pesticide, irrigation, machinery and labor (seedling nursing inputs like fertilizer, agricultural film and hack lever for ATR and DSR were also considered).

A total of 73 questionnaires were received in the investigation. After rejecting the questionnaires of pre-survey, and those from formal surveys which were incomplete, 52 valid questionnaires were kept. According to the rice planting method used, households were divided into three groups, namely ATR, MTR and DSR, which consisted of 21, 15 and 16 households, respectively.

2.3. Cost-Benefit analysis

Cost-Benefit analysis (CBA) was a means used to assess the value of a project by comparing the total cost and benefit. As an economic policy-making method, CBA could help policy makers to select the best and most efficient project, seek maximal value of the objective function, adopt an optimal resource allocation scheme, and improve the allocation efficiency. The assessed criteria were:

(1) Net present value (N_pv ): N_pv asked whether the sum of discounted gains exceeded the sum of discounted losses to judge whether the project was feasible. If gains were greater than losses, resources would be reasonably allocated. In other words, N_pv was:

$N_{pv}=\sum _{t}B_{t}{\left(1+i\right)}^{-t}-\sum _{t}C_{t}{\left(1+i\right)}^{-t}{\mbox{,}}$

( 1)

where B_t  represented discounted gains, C_t  represented discounted losses, i was discount rate, and t  ranged from 0 (the first year of the project) to T  (the last year of the project). The criterion for project acceptance was N_pv  > 0 [ Hanley and Spash , 1993 ]. Because the input and output of rice and wheat production in this study occurred in the same year, the values of t and i were set to 0.

(2) Benefit-cost ratio (B_cr ): B_cr asked the overall benefit value versus the cost value in the lifetime of the project investment. If B_cr  > 1, the investment project would be accepted. Bcr was calculated by:

$B_{cr}=\sum _{t}B_{t}{\left(1+i\right)}^{-t}/\sum _{t}C_{t}{\left(1+i\right)}^{-t}{\mbox{.}}$

( 2)

This paper applied the two standards mentioned above to assess the economic effect of different cropping measures. Social and ecological benefits were also considered, through household investigation and expert consultation, to evaluate their potentially comprehensive benefits. Social benefit was weighed in labor consumed while ecological benefit was evaluated in GHG emissions.

2.4. Estimation of anthropogenic GHG emissions

Considering the study area’s reality, the default method from 2006 IPCC Guidelines for National Greenhouse Gas Inventories, recommended by IPCC  [2006] , was employed to estimate anthropogenic GHG emissions from rice and wheat production, including GHGs from agricultural machinery fuel consumption, direct and indirect N₂ O emissions from chemical fertilizer application, CO₂ from urea application and CH₄  from paddy field flooding.

GHG emissions (including CO₂ , CH₄ and N₂ O) estimation formula for the agricultural machinery fuel consumption was as follows:

$Emissions=Fuel\cdot EF{\mbox{,}}$

( 3)

where Emissions were the amount of certain GHG (in kg), Fuel was fuel consumed (represented by fuel sold, in TJ) and EF was the emission factor for petrol and diesel (in kg TJ⁻¹ ). The default EF was 74,100 kg TJ⁻¹ for CO₂ , while 3.9 kg TJ⁻¹ for CH₄ and N₂ O.

Calculation formula for direct N₂ O emissions (in kg) from chemical fertilizer application was as follows:

$Direct\quad N_{2}O\quad emissions\quad =\quad \left[F_{SN}\cdot {EF}_{1}+\right.$$\left.{\left(F_{SN}\right)}_{FR}\cdot {\mbox{E}}{\mbox{F}}_{1FR}\right]\cdot 44/28{\mbox{,}}$

( 4)

where F_SN and (F_SN )_FR were annual amount (in kg) of synthetic fertilizer N applied to dry land and paddy field respectively, EF₁ and EF_1FR were emission factors of N₂ O caused by fertilizer N input in the two fields (in kg kg⁻¹ ). Default EF₁ was 0.01, and EF_1FR was 0.003.

Calculation formula for indirect N₂ O emissions (in kg) caused by chemical fertilizer application was as listed below:

$Indirect\quad N_{2}O\quad emissions\quad =\quad \left[F_{SN}\cdot {Frac}_{GASF}\cdot {EF}_{2}+\right.$$\left.F_{SN}\cdot {Frac}_{LEACH-\left(H\right)}\cdot {EF}_{3}\right]\cdot 44/28{\mbox{,}}$

( 5)

where F_SN was annual amount of synthetic fertilizer N applied to soils (in kg), Frac_GASF was the fraction of synthetic fertilizer N that volatilises as NH₃ and NO_X  (in kg kg⁻¹ ), EF₂ was the emission factor for N₂ O emissions from atmospheric deposition of N on soil and water surfaces (in kg kg⁻¹ ), Frac_LEACH-(H)  was the fraction of all N which was lost when added to/mineralised in managed soil in regions where leaching/runoff occurs (in kg kg⁻¹ ), and EF₃ was the emission factor for N₂ O emissions from N leaching and runoff (in kg kg⁻¹ ). Default factors: EF₂ = 0.01, EF₃ = 0.0075, Frac_GASF = 0.10, and Frac^LEACH-(H) = 0.30.

CO₂ emissions (in t) from urea application were calculated by:

${CO}_{2}\quad emissions\quad =\quad M\cdot {EF}_{4}\cdot 44/12{\mbox{,}}$

( 6)

where M  was annual amount of urea applied to field (in t), default EF₄ equaled 0.20.

CH₄ emissions (in Gg) from paddy fields were estimated by:

${CH}_{4}\quad emissions\quad =\quad {EF}_{i,j,k}\cdot t_{i,j,k}\cdot A_{i,j,k}\cdot {10}^{-6}{\mbox{,}}$

( 7)

where i , j and k  represented different ecosystems, water regimes, type and amount of organic amendments, and other conditions under which CH₄ emissions from rice may vary, EF_{i ,j ,k}  represented the daily emission factor under i , j and k  conditions (in kg hm⁻² d⁻¹ ), was cultivation period of rice under i, j and k conditions (in d), and A_{i ,j ,k}  meant annual harvested area of rice under i, j and k conditions (in hm² ). A default EF_i,j,k , which amounted to 1.28, was used in the study.

3. Results and analysis

3.1. Crop growth stages and yield

ATR seedling nursing began in early May, while MTR began in the end of May. Wheat was always harvested in early and mid- June, with the range varying less than one week. Afterwards, ATR and MTR seedlings would be transplanted to fields when DSR was also directly seeded at the same time. ATR, MTR and DSR were harvested in early, mid- and late October respectively. Wheat sowing was finished within five days after rice harvest.

In terms of yield performance (Table 1 ), MTR achieved the highest (8,115 kg hm⁻² ), ATR the second-highest (7,905 kg hm⁻² ), and DSR the lowest (7,485 kg hm⁻² ). This was in accordance with the previous agronomic experiment results in the same region [ Zhang, 2010  ;  Han et al ., 2011 ]. Average yield of wheat was the highest in MTR households (7,133 kg hm⁻² ), followed by ATR households (6,758 kg hm⁻² ). DSR households’ wheat yield was the lowest (6,033 kg hm⁻² ). The results from the multiple comparison test showed that rice yields did not vary significantly among the three groups, but ATR and MTR households’ wheat yields were significantly higher than DSR households’ yields. This implied the effect of the rice planting method on yield was not definitive and needs further research, however sowing too late had a significantly negative impact on wheat yield. Maybe this was because, late harvest of MTR and DSR could reduce yield losses to some extent although sowing periods of MTR and DSR were later than ATR, ranging from 25 to 45 d. One of the favorable factors was recent increases in temperatures and shorter winters [ Zhang et al. , 2011 ], which has extended the rice appropriate growth period. Sowing wheat too early or too late leads to vigorous growth or weak seedlings before winter, which is adverse to increasing and stabilising the yield. The sowing date experiment on the main wheat cultivar Yannong 19 in the same area, finished by Shen and Ji  [2012] is an excellent demonstration of it. Therefore, wheat-rice Double Late mode has a good development prospect, but needs to be duly exploited according to climate resources.

Table 1. Yields of rice and wheat in 2010 and 2011 (in kg hm ⁻² )

Household	Rice			Wheat
	2010	2011	Mean	2010	2011	Mean
ATR	7,815a	7,995a	7,905a	6,810a	6,705ab	6,758a
MTR	8,070a	8,160a	8,115a	7,140a	7,125ac	7,133a
DSR	7,440a	7,530a	7,485a	5,955b	6,090b	6,033b

Note: Letters difference in the same column indicate significant difference between groups (P < 0.05)

3.2. Social benefit

In terms of increasing yield and ensuring food supply, MTR ranked highest, followed by ATR and then DSR. On the aspect of labor, the amount of input used during the wheat production processes in all groups was basically the same (30 d hm⁻² ) due to the use of same sowing method, direct seeding. MTR saw progress in transplanting efficiency relative to ATR. DSR on the other hand, though leaving out the seedling nursing and transplanting processes, required more labor to manage the rice field because of other problems, such as low emergence rate and excessive amounts of weeds. In the overall production process, the labor requirements of ATR, MTR and DSR were 94.4, 66.8 and 57.2 d hm⁻² respectively, meaning MTR and DSR required 27.6 and 37.2 d hm⁻² less than ATR, which could be converted to 1,656 and 2,232 RMB hm⁻² while local labor price was 60 RMB d⁻¹ . Thus, social benefits generated by MTR and DSR were obviously more.

3.3. Economic benefit

As shown in Table 2 , labor, fertilizer, machinery, and irrigation were the primary costs among the seven input factors of rice production and higher than 1,000 RMB hm⁻² . Labor cost for ATR, MTR and DSR accounted for 42.7%, 24.7% and 22.3% of total cost respectively. For wheat production, fertilizer, labor, machinery and seeds were the four main costs, and also higher than 1,000 RMB hm⁻² .

Table 2. Average costs and benefits of rice and wheat in 2010 and 2011

Household	Crop	Seed	Seedling nursing	Fertilizer	Pesticide	Irrigation	Machinery	Labor	Total cost	Total benefit	Npv	Bcr
ATR	Rice	1,040	593	2,423	727	1,170	1,643	5,664	13,260	20,196	6,936	1.52
	Wheat	1,189	0	3,703	918	314	1,504	1,817	9,445	13,723	4,278	1.45
	Total	2,229	593	6,126	1,645	1,484	3,147	7,480	22,705	33,918	11,214	1.49
MTR	Rice	882	411	3,383	1,034	2,528	4,008	4,009	16,254	20,947	4,694	1.29
	Wheat	1,320	0	4,069	745	708	2,067	2,073	10,982	14,599	3,617	1.33
	Total	2,202	411	7,452	1,779	3,235	6,074	6,082	27,235	35,546	8,311	1.31
DSR	Rice	904	0	3,834	1,005	4,838	1,407	3,431	15,420	19,598	4,178	1.27
	Wheat	1,697	0	3,259	855	829	1,378	1,832	9,850	11,804	1,953	1.20
	Total	2,601	0	7,093	1,860	5,668	2,785	5,263	25,270	31,401	6,131	1.24

Note: Unit in each column is RMB hm⁻² except B_cr which has no unit

From 2010 to 2011, the average gains of using MTR (in rice production) were the highest (20,947 RMB hm⁻² ), and N_pv the second (4,694 RMB hm⁻² ). For ATR, average income was less (20,196 RMB hm⁻² ), but N_pv the highest (6,936 RMB hm⁻² ). Not only average gains of DSR were the lowest (19,598 RMB hm⁻² ), but also N_pv  (4,178 RMB hm⁻² ), meaning only 89.0% and 60.2% of MTR’s and ATR’s N_pv . The order of rice B_cr  from high to low was ATR (1.52), MTR (1.29), DSR (1.27).

Average gains of households using MTR in wheat production were the highest (14,599 RMB hm⁻² ), and N_pv the second (3,617 RMB hm⁻² ). For ATR households’ average income from wheat was less (13,723 RMB hm⁻² ), but N_pv the highest (4,278 RMB hm⁻² ). Not only average gains from the use of DSR was the lowest (11,804 RMB hm⁻² ), but also N_pv  (1,953 RMB hm⁻² ), meaning only 54.0% and 45.6% of MTR and ATR households’ N_pv . The order of wheat B_cr from high to low was ATR households (1.45), MTR households (1.33), and DSR households (1.20).

After combining rice with wheat, DSR households’ N_pv was the lowest (6,131 RMB hm⁻² ), MTR households the second (8,311 RMB hm⁻² ), and ATR households was the highest (11,214 RMB hm⁻² ), meaning 2,903 and 5,083 RMB hm⁻² higher than MTR households and DSR households respectively. The order of B_cr from high to low was ATR households (1.49), MTR households (1.31), and DSR households (1.24). These results show that the economic benefit of wheat-rice rotation and multiple cropping mode of action with ATR was most optimal, followed by mode of action with MTR, leaving mode of action with DSR as the worst.

3.4. Ecological benefit

Agricultural machinery was applied in crop production processes, such as tillage, irrigation, harvest, transportation and transplant, and it would consume fuel, leading to GHG emissions. To compare the ecological benefits among groups conveniently, all GHG emissions were converted into their CO₂ equivalent [ ECSCNARCC , 2011 ]. Diesel consumption during wheat production was estimated to be 162.0 L hm⁻² , and petrol/diesel consumptions of ATR, MTR and DSR production were 391.5, 457.5, and 402.0 L hm⁻²  respectively (Table 3 ). According to the estimation method presented above, GHG emissions caused by agricultural machinery in a rice-wheat rotation period were 1,494.45, 1,672.65, and 1,522.80 kg hm⁻² respectively for ATR, MTR and DSR households. GHG emissions contributed by mechanical transplanting of rice were 81 kg hm⁻² .

Table 3. Estimated petrol/diesel consumptions in rice and wheat production (in L hm⁻² )

Crop	Tillage	Irrigation	Harvest	Transport	Transplant	Total
Wheat	22.5	112.5	19.5	7.5	0.0	162.0
ATR	37.5	324.0	22.5	7.5	0.0	391.5
MTR	37.5	360.0	22.5	7.5	30.0	457.5
DSR	37.5	334.5	22.5	7.5	0.0	402.0

Chemical fertilizer application and flooding irrigation in the rice-wheat planting processes could result in direct and indirect emissions of N₂ O, CO₂ and CH₄ . These kinds of GHG emissions in a year were 7,165.95, 8,093.73, and 7,791.81 kg hm⁻² in total, respectively for ATR, MTR and DSR households. The order of paddy field CH₄ emissions from high to low was DSR (165.15 kg hm⁻² ), MTR (156.15 kg hm⁻² ) and ATR (148.50 kg hm⁻² ) (Table 4 ). This could be attributed to the fact that, more chemical fertilizer was applied, and the period of water-flooding was longer for DSR comparatively.

Table 4. GHG emissions caused by chemical fertilizer applications (in kg hm⁻² )

Household	Crop	Application		Emissions
		Fertilizer N	Urea	N2 O	CO2	Rice CH4	GHG (CO2 -eq)
ATR	Rice	298.5	373.5	2.93	273.90	148.50	4,301.23
	Wheat	376.5	592.5	7.84	434.55	0.00	2,864.72
	Total	675.0	966.0	10.77	708.45	148.50	7,165.95
MTR	Rice	405.0	498.0	3.98	365.25	156.15	4,877.48
	Wheat	424.5	649.5	8.84	476.25	0.00	3,216.25
	Total	829.5	1,147.5	12.82	841.50	156.15	8,093.73
DSR	Rice	406.5	588.0	3.99	431.25	165.15	5,137.05
	Wheat	352.5	517.5	7.34	379.50	0.00	2,654.76
	Total	759.0	1,105.5	11.33	810.75	165.15	7,791.81

In a rice-wheat rotation cycle, the total anthropogenic GHG emissions caused by agricultural machinery fuel consumption, chemical fertilizer application and paddy field flooding were 8,660.40, 9,766.38, and 9,314.61 kg hm⁻² respectively for ATR, MTR and DSR households, meaning GHG emission intensities per unit of N_pv were 0.77, 1.18 and 1.52 kg per RMB, respectively. The order of GHG emission intensities per unit of rice yield from high to low was DSR (0.83 kg kg⁻¹ ), MTR (0.75 kg kg⁻¹ ), and ATR (0.68 kg kg⁻¹ ), and the same order was true for wheat yield, though with lower intensities: DSR (0.51 kg kg⁻¹ ), MTR (0.51 kg kg⁻¹ ), and ATR (0.49 kg kg⁻¹ ). In summary, ATR and its rice-wheat rotation mode scored the lowest among the three cropping patterns no matter which aspect, GHG emission intensities of per unit area, N_pv or yield, was used to compare with.

4. Conclusions and discussion

(1) In terms of rice yield performance, MTR was the best (8,115 kg hm⁻² ), ATR the second (7,905 kg hm⁻² ), and DSR the worst (7,485 kg hm⁻² ). For wheat yield, MTR households scored the highest (7,133 kg hm⁻² ), followed by ATR households (6,758 kg hm⁻² ), and DSR households the lowest (6,033 kg hm⁻² ). The impact of rice planting method on yield was not completely clear and needs further studies. However, sowing too late had a significantly negative impact on wheat yield.

(2) On the aspect of social benefit, the yield variations indicated various contributions of different cropping patterns on food supply. Comparing with the traditional rice planting method of artificial transplanting, direct seeding and mechanical transplanting cut down labor input and achieved obvious social benefit, which could be converted to 2,232 and 1,656 RMB hm⁻² .

(3) If compared by economic benefit, ATR performed best. The order of rice B_cr from high to low was ATR (1.52), MTR (1.29), and DSR (1.27). After combining rice with wheat, DSR households’ N_pv was the lowest (6,131 RMB hm⁻² ), MTR households’ the second (8,311 RMB hm⁻² ), and ATR households’ was the highest (11,214 RMB hm⁻² ), meaning 2,903 and 5,083 RMB hm⁻² higher than MTR and DSR households. Also, wheat-rice rotation mode with ATR achieved the most economic benefits, accompanied by the order of B_cr  from high to low, which was ATR households (1.49), MTR households (1.31), and DSR households (1.24).

(4) Farming measures like agricultural machinery fuel consumption, chemical fertilizer application and paddy field flooding resulted excessive anthropogenic GHG emissions. No matter which aspect (GHG emission intensities of per unit area, N_pv or yield) was used to compare, DSR and MTR households’ were higher than ATR households’. ATR and its wheat-rice rotation and multiple cropping mode of action not only resulted in the most comprehensive benefits, but also helped to reduce anthropogenic GHG emissions and mitigate global warming.

(5) If the increased available agricultural climate resources are timely exploited, the wheat-rice Double Late mode could have good application prospects. The cropping pattern of combining the wheat-rice Double Late mode with the ATR is a better choice to mitigate and adapt to climate change.

It should be noted that these benefits were calculated or estimated from temporal prices of agricultural materials, labor and grain, and the technology level of seed, irrigation, machinery and chemical fertilizer application at the time. With future policy regulations on food supply and ecological environment, market fluctuations and technological progress, these cropping patterns’ economic, social and ecological benefits will change, as well as their priority to adapt to climate change. What’s more, the study only presented two years’ investigation results, thus long-term cost and benefit data needs to be collected. GHG emissions, especially the CH₄ emission factors of paddy fields, varied by planting method, however, the same set of emission factors from IPCC methodology was used to estimate GHG emissions from ATR, MTR and DSR fields because of a lack of local GHG emission factors supported by literatures and experiments. All of these elements could give rise to uncertainties in the research results.

Acknowledgements

This research was funded by the National Basic Research Program of China (No. 2012CB955904), the Sustainable Agriculture Innovation Network initiated and funded by Defra UK and Ministry of Agriculture of China (No. H5105000), and China’s National Agricultural Comprehensive Development Office.

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