Yield of rice as affected by transplanting dates and plant spacing under climate change simulations

Transcription

1 Wudpecker Journal of Agricultural Research ISSN Vol. 2(12, pp , February Wudpecker Journals Yield of rice as affected by transplanting dates and plant spacing under climate change simulations Oteng-Darko P 1, Kyei-Baffour N. 2 and Ofori E. 2 1 CSIR-Crops Research Institute, P. O. Box 785, Kumasi, Ghana. 2 Department of Agricultural Engineering, KNUST, Kumasi, Ghana. *Corresponding author patamankwaa@yahoo.com Accepted 09 November 2012 Climate change is impacting negatively on livelihoods in Africa as evidenced by the misery caused by floods and droughts which are occurring with increasing frequency and severity. Thus durable strategies should be devised that would assist African communities particularly rural communities to adapt to climate change-induced yield reductions in crop production. In view of the importance of rice (Oryza sativa L.) as both as a food and cash crop in Sub-Sahara Africa, conducting research on the mitigating effects of varying planting dates and transplant ages on rice can have a dramatic effect on crop development and yield. This research was conducted to study the yield response of transplanted rice to different dates and transplant ages. Simulations were conducted using the CERES-Rice model under prevailing weather conditions and future scenarios of climate. The simulation run for the climate change scenario showed adverse effects on crop yield. The rice crop model generally predicted yield reductions due to possible climate change in the future. After examining alternative management practices (transplant age and crop spacing), it became evident that changes in planting dates, crop spacing, and transplanting dates could improve grain yield. The increased maximum and minimum temperatures, irrespective of whether CO 2 concentration was increased or not, seemed to have more adverse effects on the rice yield. Key words: Weather conditions, adverse effects, transplanted rice, CO 2 concentration, climate change. INTRODUCTION Rice is increasingly becoming the most important food crop in Ghana mainly due to urbanization, its availability on the market all year round and the ease with which it can be cooked. Rice has been in cultivation in Ghana for a very long time. In the 17th and 18th centuries, rice was one of the major commercial food crops (Mobil and Okran, 1985). Presently, rice is one of the major cereals in Ghana. The Ghanaian rice self-sufficiency rate is about 41% (WARDA, 1986), resulting in high annual imports costing around seven million dollars to meet the growing demand (Andriesse and Fresco, 1991). However, production of rice, as for other crops, is beset by constraints such as drought, flooding, salt stress and extreme temperatures, all of which are expected to worsen with climate change. Drastic changes in rainfall patterns coupled with rising temperatures will introduce unfavorable growing conditions (due to drought, flooding, and etc.) into cropping calendars thereby modifying growing seasons which could subsequently reduce crop productivity. This is particularly true of West Africa s Sahelian region. There is large consensus that in west Africa one of the major climate change impacts will be on rainfall, making it more variable and less reliable. This will affect the onset and length of growing seasons, particularly in semi-arid areas where yields from rain-fed agriculture could be reduced by up to 20 to 50% by 2050 (Sarr et al., 2007). Greater climate variability which incorporates the later onset, higher temperatures and increased potential evapotranspiration will make farming systems more highly vulnerable to climate change. Also, transition zones between areas characterized by monomodal and bimodal rainfall, as well as areas with bimodal rainfall already experience problems associated with cropping periods (Diouf et al., 2007). The increase in temperatures is also likely to reduce the duration of the hot off-season period for irrigated rice farming, because of increased risk of sterility due to high temperatures at flowering stage. Climate change will significantly affect food production and requires immediate and ongoing

2 Darko et al. 056 adaptation (Gornall et al., 2010). A wide range of adaptive actions can be implemented to reduce or overcome some negative effects of climate change on agriculture. Famers have already adapted a wide range of techniques intended to increase crop yield (Barbier et al., 2008). Among the crop production tools, proper time and method of are the prerequisites that allow the crop to complete its life phase timely and successfully under a specific agro-ecology. For successful rice production, timely planting, appropriate control of vegetative growth throughout the duration of the crop, suitable transplanting densities for optimum tillering and control of leaf growth by controlling water, fertilizer and chemical inputs are essential for improving the growth variables responsible for high yield (Ghosh and Singh, 1998). Planting date can have a dramatic effect on crop development and yield. The response of rice cultivars to planting date is important when selecting the most appropriate cultivar for a particular planting date. Oteng (1994) identified late planting among many constraints in rice production in Ghana. In rice, the optimum leaf areas for seedlings, optimum leaf shapes to maximize photosynthetic efficiency, deep, well-developed root systems, leaf area index (LAI) at flowering and crop growth rate (CGR) during panicle initiation have been identified as the major determinants of yield (Sun et al., 1999). A combination of these growth variables explains variations in yield better than any individual growth variable (Ghosh and Singh, 1998). Similarly, Thakur and Patel (1998) reported that dry matter production, leaf area index, leaf area duration (LAD), crop growth rate, net assimilation rate (NAR) and relative growth rate (RGR) are ultimately reflected in higher grain yield of rice. Lu et al., (1999) obtained higher yield of rice due to higher net assimilation rate and better distribution of leaf area index after heading. Among the improved cultural practices, seedling age and number of seedling hill -1 can play important roles in boosting yield of rice. Age of seedling is an important factor as it has tremendous influence on the tiller production, grain formation and other yield contributing characteristics (BRRI, 1981). Generally, farmers of Ghana do not give due attention to the age of seedlings at transplanting and use aged seedling. The use of overaged seedling retard the general performance of crop and the yield of the crop reduces drastically as the farmers are not aware of this factor for rice production. For optimum yield, age of seedlings at transplanting of a particular variety at a particular season may not be suitable for other varieties at other season. So, it is very important to find out the optimum age of seedling of a particular variety for a particular season. The optimum plant density depends on different factors that most importance of this factors include: plant characteristics, growth period duration, planting time and methods, soil fertility, plant size, available moisture, sunshine, planting pattern and situation of weeds (Shirtliffe and Johnston, 2002). Plant spacing is an important production factor in transplanted rice (Gorgy, 2010). Mohapatra et al., (1989) reported that plant spacing of cm was better than those of or cm under normal soil for rice productivity. Maske et al., (1997), reported that plant height, leaf area index, yield and yield components of rice with plant spacing of cm were higher than of cm or cm. Patel (1999), observed that hill spacing of cm as compared with cm and cm of hill spacing recorded perceptible increase in number of panicles per m, yield and straw yield. Mohammadian et al., (2011), studied yield and yield components of rice variety Ali Kazemi under different plant spacing and number of seedlings per hill. It was reported that the highest grain yield of 5582 kg/ha was obtained from plant spacing of cm. As an advantage to farmers, forecasting yield under climate change scenarios will simultaneously give favorable planting dates on which to plant in order to maximize yield. Also in the event of sudden climate change scenarios, simulation may show yields of unprofitable levels and there may be the need to grow an alternative crop for the season. In Ghana, some farmers plant rice in June to July, after the rains have established (especially those in the inland valleys), others plant between March and April, at the onset of the major rains (this is related to upland hill rice ecology) (Donya, 2000). While inland valley rice takes to 4 months to harvest, upland hill rice takes about 4 to 5 months. This is due to the variety used. The study aimed to investigate the influence of different plant spacing and transplant age on rice yield under varying climate change scenarios in order to select the optimum planting pattern for cultivation. MATERIALS AND METHODS Selection of simulation model The CERES-Rice simulation model was selected because of its widespread use and validation. Also internal parameter settings such as the genetics and phenology components of the CERES-Rice model are subject to modification by the user, hence, specifying some variety-specific parameters to enable the assessment of yield results under localized conditions. Data collection Climatic data were sourced from the Meteorological Services Agency. Since the Anum Valley Irrigation Project did not have a meteorological station, climate data from the Bobiri forest reserve were used because of its proximity to the project site. An eighteen year period of information ranging from was obtained from the station.

3 057 Wudpecker J. Agric. Res. Soil data were obtained from the office at the project site. The management and crop data were obtained from the Crop Research Institute of the Council for Scientific and Industrial Research (CSIR), Fumesua. Two different sets of questionnaires were developed for the study. The first set was administered to the farmers to source management and crop information, while the second was administered to researchers at the Crops Research Institute. Model calibration, validation and yield simulations Model calibration and validation were done as described by Oteng-Darko et al., (2012). Experimental data file was created using XBuild. The file contained details of the experiment (field characteristics, soil analysis data, initial soil water and inorganic nitrogen conditions, seedbed preparation and planting geometries, irrigation and water management, fertilizer management, organic residue applications, chemical applications, tillage operations, environmental modifications, harvest management), simulation controls, and treatment combinations. This data were saved and imported into the rice model. The run icon was selected to initiate the running of the model. Upon initiation, created climate, crop and soil files along with the experimental file were recalled for the simulations. Investigation of planting dates for best yields To mitigate the impacts of climate change on rice productivity and sustain crop yield with changing climatic scenarios, the seasonal analysis driver of DSSAT was run with 5 planting dates selected at one week interval for all the months of the year using 2006 as the base year. Fertilizer application amount was also increased to the recommended amount of 90kgN/ha. The selected planting dates were used to run different simulations, and the dates giving the best yield results were selected. Investigation of the best management practices Transplant age and planting dates were used as treatments to run simulations under the current climate change scenarios, and data were analyzed for the best management practices. Also, environmental modifications in maximum and minimum temperature, solar radiation and CO 2 concentration were made to the current weather data, and simulations were run using planting date, transplant age and plant spacing as treatments. The results were analyzed, and recommendations were made as to the best management practice under the climate change scenarios. Input Parameters The following were the input data used for the validation and running of the model (unless otherwise stated for a particular scenario): Plot size 50 m x 20 m (0.1 ha) Top soil loamy sand Bulk density 1.20g/cm Soil ph 5.78 Climate data daily weather data (maximum and minimum temperatures, rainfall and solar radiation) from from the Bobiri Forest Reserve CO 2 concentration 0 ppm (parts per million) Planting method transplanting Planting depth 5 cm Irrigation no water stress Fertilizer amount 90 kgn/ha Harvest at maturity (120 days) For the climate change scenario, temperatures were increased by 4 o C, while CO 2 concentration was increased by 20 ppm. RESULTS Planting done in April (Figure 1) gave the highest simulated yields for all the planting dates selected with the minimum of 6.52 t/ha observed when planting was done on April 1 st at a transplant age of 7 days and a maximum simulated yield of 7.46 t/ha occurring when planting was done on the 22 nd and transplanted after 21days of. The months of January, February and March showed an increasing trend in yield when transplanting was done 28. If planting is done in any of these months, transplanting is recommended to be done after 28 days of. The months of April and May showed an undulating trend in simulated yield with the highest simulated yield of 7.46 t/ha recorded when planting is done on the 22 nd of April and transplanted 28. However, the month of May showed increasingly high yields when transplanting was done 21. Transplanting 14 is not favorable for the months of April, May and June. There was a yield reduction during the months of July and August for all the transplant ages selected. However, if planting is to be done in any of these months a transplant age of 21 days is recommended to avoid yield uncertainties. The yield increases in September with the highest equaling that of April with a simulated yield of 7.46 t/ha when planting is done on the 8 th and transplanted 28. Planting in October, November and December would not be advised since simulations done in these months showed a decreasing trend in yield for all the selected transplanting dates.

4 Darko et al transplanted 14 transplanted 21 transplanted Jan 01-Feb 01-Mar 01-Apr 01-May Planting 01-Jun 01-Jul 01-Aug 01-Sep 01-Oct 01-Nov 01-Dec Figure 1. Effects of varying planting dates and transplant age on grain yield under prevailing climatic conditions Jan 01-Feb 01-Mar 01-Apr 01-May 01-Jun 01-Jul 01-Aug 01-Sep 01-Oct 01-Nov 01-Dec transplanted 7 transplanted 14 transplanted 21 Figure 2. Chart showing simulated yields for January to December using a plant spacing of 10cm. To crop once a year, planting on the 22nd day of April would give the best yield of 7.46 t/ha, but since it is possible to crop twice a year under the project s irrigation scheme, planting on the 22 nd day of April would give a yield of 7.46 t/ha and maturity date of 21 st August. This would give room for harvesting, land and nursery preparation for another planting on the 22nd September which would also give a relatively high yield of 7.4 t/ha. The poorest yields were however observed when planting was done in December and January. The highest yield recorded when planting was done in January was 4.99 t/ha (Figure 2) and this occurred when planting was done on January 1, spaced 10cm apart and transplanted 28. The dry month of January calls for closer planting of the rice seedlings inorder to increase mutual shading and regulate the use of solar radiation for photosynthesis. Earlier simulations done using planting dates and transplant age as treatment without any change in climate scenarios gave the highest yield for January to be 6.01 t/ha when planting was done on the 8 th. There, however, appears a yield reduction of 20.44% between these two highest

5 059 Wudpecker J. Agric. Res. yields of January (one with climate change and the other without climate change or under prevailing weather conditions). The highest yield gap of 0.66% observed in March occurred when planting was done on March 15 th under the climate change scenario simulations. The least yield gap of 19.94% was observed when planting was done on the 28 th of March and spaced 10cm apart under the climate change scenario. Planting on the 28 th of March would therefore be recommended to farmers who wish to plant in March. The highest yield recorded when planting was done in May was 5.86 t/ha (Figure 2) and this occurred when planting was done on May 28 th, spaced 10cm apart and transplanted 28. Earlier simulations done using planting dates and transplant age as treatment without any change in climate scenarios gave the highest yield for May to be 7.41 t/ha when planting was done on the 15 th. There, however, appears a yield reduction of 20.92% between these two highest yields of January (one run under climate change scenario simulations and the other run without climate change modifications or under prevailing weather conditions). The highest crop yield reduction recorded for January was 7% and it occurred between the lowest yield 5.02 t/ha recorded under the prevailing weather conditions scenario and the lowest yield (.70 t/ha) recorded under the climate change scenario simulations. These differences implies that yield reductions can go as high as 62.4% (comparing the highest crop yield of 6.01 t/ha recorded when planting is done on the 8 th of January and transplanted after 28 days of under prevailing weather conditions and the lowest crop yield of.70 t/ha recorded when planting is done on the 1 st of January and transplanted 28 under the climate change scenario simulations). The month of February (Figure ) showed appreciable crop yield increases over those of January. The highest yield reduction of 19.4% was recorded for a crop yield of 6.69 t/ha when planting was done on the 22 nd of February and transplanted 28 under prevailing weather conditions simulations and a crop yield of 5.9 t/ha when planting was done on the 28 th February and transplanted 28 and spaced 15cm apart under the climate change scenario simulations. Yield reduction as high as 8.56% can however be experienced if planting is done on the planting date that gave the best yield under prevailing conditions simulations and the planting date that gave the lowest yield under the climate change scenarios simulations. The lowest yield reduction of 20.51% for April was recorded for a crop yield of 7.46 t/ha when planting was done on the 22 nd of April and transplanted 21 under prevailing weather conditions simulations and a crop yield of 5.9 t/ha when planting was done on the 28 th, spaced 20 cm apart and transplanted 28 days (Figure 4) after under the climate change scenario simulations. The highest yield reduction of 28.22% was recorded when planting was done on the 22 nd of June and transplanted 14 under prevailing weather conditions simulations and a crop yield of 5.97 t/ha when planting was done on the 1 st June, spaced 20 cm apart and transplanted 7 under the climate change scenario simulations. Yield reduction as high as 6.51% can however be experienced if planting is done on the 1st of June, spaced 25cm apart and transplanted 14 under the climate change scenarios. July produced favourable yields under the climate change scenarios simulations. Planting in August so far gave the least recorded yield gap of 8.55% when planting was done on the 1 st and 28 th with a simulated yield of 5.99 t/ha. Planting in August is therefore recommended when planting is done under any of the conditions used in the climate change scenarios. The lowest yield gap of 2.02 % was recorded for September when planting was done on the 15 th, spaced 10cm apart and transplanted 28 whilst the highest yield gap of 9.87 was recorded when planting was done on the 15 th spaced 20cm apart and transplanted 28. October gave one of the most shocking yield gaps recorded. This yield gap (56.75%) was recorded when planting was done on the 28 th day of October, spaced 25 cm apart and transplanted after 21 days of. Planting in October in general, gave one of the worst yields recorded under the climate change simulations hence planting in October would be totally discourage among rice farmers on the project. The least recorded yield for November occurred when planting was done on the 15 th, spaced 25 cm apart and transplanted 14 days after. November had its lowest yield gap as 18.99% and the highest been 40.42%. The poorest yields under the climate change scenarios were observed when planting was done in December (Figure 6). the lowest yield of.11 t/ha was observed when planting was done on the 28 th of December, spaced 0cm apart and transplanted 7. The lowest yield gap for the month of December was 40.95%. In the month of January, the highest yield reduction occurred when planting was done on the 1 st and transplanted after 14 days of with a plant spacing of 0cm. The best yield of 5.99 t/ha would be achieved when planting is done on August 1st and transplanted 28 days after with a crop spacing of 15cm. The yield gap for February would best be minimised if planting is done on the 28 th, transplanted after 28 days and spaced 10cm apart. The highest yield for March under the climate change scenarios was 5.81 t/ha. This occurred when planting was done on the 28 th day of March, transplanted 28 and spaced 15cm apart. The least affected months were March, April and August. August recorded the maximum simulated yield of 5.99 t/ha under the climate change scenarios when planting was done on

6 Darko et al transplanted 7 days after transplanted 14 transplanted 21 transplanted Jan 01-Feb 01-Mar 01-Apr 01-May 01-Jun 01-Jul 01-Aug 01-Sep 01-Oct 01-Nov 01-Dec Figure. Chart showing simulated yields for January to December using a plant spacing of 15cm transplanted 7 transplanted 14 transplanted Jan 01-Feb 01-Mar 01-Apr 01-May 01-Jun 01-Jul 01-Aug 01-Sep 01-Oct 01-Nov 01-Dec Figure 4. Chart showing simulated yields for January to December using a plant spacing of 20cm

7 061 Wudpecker J. Agric. Res. Simaulated yield, t/ha transplanted 7 days after transplanted 14 days after transplanted 21 days after transplanted 28 days after Figure 5. Chart showing simulated yields for January to December using a plant spacing of 25cm transplanted 7 days after transplanted 14 transplanted 21 transplanted 28 Figure 6. Chart showing simulated yields for January to December using a plant spacing of 0cm. the 1 st and transplanted 28 with a plant spacing of 10cm. April also recorded a simulated yield of 5.9t/ha on the 28 th at a plant spacing of 10cm and transplant age of 28 days. DISCUSSION Crop yields in a specific area are expected to vary due to variation in weather conditions between years and within seasons. The simulation run for the climate change scenario showed adverse effects on crop yield. The rice crop model generally predicted yield reductions due to possible climate change in the future. After examining alternative management practices (transplant age andcrop spacing), it became evident that changes in planting dates, crop spacing and transplanting dates could improve grain yield. Figures 1, 2,, 4, 5 and 6 depict the changing trends in crop yields over the various months of the year under climate change scenario simulations. Significant reduction was simulated when the rice crop was planted at inappropriate times (as shown by

8 Darko et al. 062 varying planting dates). Increased rainfall increased rice yield even under irrigated conditions. The increased maximum and minimum temperatures, irrespective of whether CO 2 concentration was increased or not, seemed to have more adverse effects on the rice yield If temperatures as were used in the climate change scenarios are experienced in the years to come, then farmers are advised to start their first season s planting on March 1st with a 90kgN/ha of applied Nitrogen, plant spacing of 15 cm and transplanted 28. This would give a harvest date on the rd of July thereby giving room for land preparation and nursery for the second season s planting on August 28 th. It is natural process that the crop which had taken more number of days from seeding to maturity might have a more vigorous and extensive root system, increased growth rate during vegetative growth, more efficient sink formation and greater sink size, greater carbohydrate translocation from vegetative plant parts to the spikelets and longer leaf area index during grain filling period (Diouf et al., 2000). So, this might be the possible reason to have high yields in earlier transplanting (Shah and Yadav, 2001). Conclusion Closer investigations of model processes and more testing of the models would be required to better examine the sensitivity to various climatic parameters and to adapt to climate change in the future. Very effective extension systems should be put in place to provide alternate training skills for farmers especially in the areas of fertiliser amounts and timing, transplanting age and plant spacing. As has been shown in this study, a shift in planting dates is required to maximise rice yield. Since increased temperatures reduce yield of rice, more temperature tolerant varieties of rice should be developed. Increased temperatures also decrease growth duration therefore breeding of early maturing varieties of rice should be done. Since irrigation is traditionally part of rice cultivation in Ghana, policies to rehabilitate and restore irrigation systems should be put in place. REFERENCES Andriesse W, Fresio LO (1991). A Characterization of rice growing Environment in West Africa. In: Agricultural Ecosystem and Environment : BRRI (Bangladesh Rice Research Institute). (1981). Annual Report for Bangladesh Rice Research Institute: 9-8 Barbier B, Yacouba H, Karambiri H, Zorome M, Some B (2008). Human vulnerability to climate variability in the Sahel: Farmers adaptation strategies in northern Burkina Faso. Environ. Manage., 4: Diouf M, Nonguierma A, Abou A, Royer A, Some B (2000). Fight against drought in Sahel: achievements and perspective. Agrhymet Regional Centre. Drought 4: (in French) Donya D (2000). Characteristics of traditional rice production in Ghana. Proceedings of the National Workshop on Rice Production in Ghana, held in Ghana. November 2000: Ghosh DC, Singh BP (1998). Crop growth modeling for wetland rice management. Environ and Ecol., 16(2): Gorgy RN (2010). Effect of transplanting spacings and nitrogen levels on growth, yield and nitrogen use efficiency of some promising rice varieties. J. Agric. Res., Kafer El-Shiekh Univ 6(2): Gornall J, Betts R, Burke E, Clark R, Camp J, Willett K, Wiltshire A (2010). Implications of climate change for agricultural productivity in the early twenty-first century. Philosophical Transactions of the Royal Society 65: Intergovernmental Panel on Climate Change, IPCC (2001). The regional Impacts of Climate change. Lu Q, Jiang D, Weng XY, Xi HF (1999). The effect of potassium nutrition on dry matter production and photosynthesis of different genotypes of rice. J Zhejiang Agric Univ., 25(): (In Chinese) Maske NS, Borkar SI, Rajgire HJ (1997). Effects of nitrogen levels on growth, yield and grain quality of rice. J. Soils and Crops 7: Mobil J, Okran VK (1985). Trends in the Production and Consumption of rice in Ghana, Accra. Mohammadian R Azarpour NE, Moradi M (2011). Study of yield and yield components of rice in different Plant Spacings and Number of Seedlings per Hill. Middle-East J. Scientific Res., 7(2): Mohapatra AK, Kar PC, Behura B, Maity K (1989). Effect of spacing, seedling per hill and nitrogen levels on growth, yield attributes and yield of CR1009 rice. Environment and Ecol., 7(1): Oteng JW (1994). Research Strategies for Improved Rice Production in Ghana. Proc. First International Seminar on Towards Rice Self Sufficiency in Ghana. Akosombo, Ghana. 7-8 June 1994 Oteng-Darko P, Kyei-Baffour N, Ofori E (2012). Simulating Rice Yields under Climate Change Scenarios using the Ceres-Rice Model. African Crop Sci. J., 20(2): Uganda Patel JR (1999). Response of rice (Oryza sativa) to time of transplanting, spacing and age of seedlings. Indian J. Agron., 44(2): Sarr B, Traorˊe S, Salack S (2007). Evaluation of the impact of climate change on yields of cereal crops in Sudano-Sahelian. International Conference for the reduction of the vulnerability of natural systems to economical esociaux. West Africa Climate Change,

9 06 Wudpecker J. Agric. Res. Ouagadougou, 27 (In French) Shah LM, Yadav R (2001). Response of Rice Varieties to age of seedlings and Transplanting dates. Nepal Agric. Res. J., 4 & 5: Shirtliffe, SJ, AM Johnston (2002). Yield density relationships and optimum plant populations in two cultivars of solid-seeding dry bean grown in Saskatchewan. Canadian J. Plant Sci., 82: Sun YF, Liang JM, Ye J, Zhu WY (1999). Cultivation of super-high yielding rice plants. China Rice 5: 8-9. Thakur DS, Patel SR (1998) Growth and sink potential of rice as influenced by the split application of potassium with FYM in inceptisols of eastern central India. J. Potassium Res., 14(1/4):7-77. WARDA (1986). Annual Report.