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Application of the Hybrid-Maize Model for Limits to Maize Productivity Analysis in a Semiarid Environment

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Application of the Hybrid-Maize Model for Limits to Maize Productivity Analysis in a Semiarid Environment 300 Liu et al. Hybrid-MaizeScientia model analysis Agricola for maize productivity Application of the Hybrid-Maize model for limits to maize productivity analysis in a semiarid environment Yi Liu1,2, Shenjiao Yang2,3, Shiqing Li2, Fang Chen1* 1Chinese Academy of Sciences/Wuhan Botanical Garden – ABSTRACT: Effects of meteorological variables on crop production can be evaluated using vari- Lab. of Aquatic Botany and Watershed Ecology – 430074 ous models. We have evaluated the ability of the Hybrid-Maize model to simulate growth, de- – Wuhan – China. velopment and grain yield of maize (Zea mays L.) cultivated on the Loess Plateau, China, and 2Chinese Academy of Sciences and Ministry of Water applied it to assess effects of meteorological variations on the performance of maize under Resource/Institute of Soil and Water Conservation – State rain-fed and irrigated conditions. The model was calibrated and evaluated with data obtained Key Lab. of Soil Erosion and Dryland Farming on the Loess from field experiments performed in 2007 and 2008, then applied to yield determinants using Plateau – 712100 – Yangling – China. daily weather data for 2005-2009, in simulations under both rain-fed and irrigated conditions. 3Chinese Academy of Agricultural Sciences/Farmland The model accurately simulated Leaf Area Index , biomass, and soil water data from the field Irrigation Research Institute – 453003 – Xinxiang – China. experiments in both years, with normalized percentage root mean square errors < 25 %. Gr.Y *Corresponding author <[email protected]> and yield components were also accurately simulated, with prediction deviations ranging from -2.3 % to 22.0 % for both years. According to the simulations, the maize potential productivity Edited by: Thomas Kumke averaged 9.7 t ha−1 under rain-fed conditions and 11.53 t ha−1 under irrigated conditions, and the average rain-fed yield was 1.83 t ha−1 less than the average potential yield with irrigation. Soil moisture status analysis demonstrated that substantial potential yield may have been lost due to Received September 14, 2011 water stress under rain-fed conditions. Accepted February 14, 2012 Keywords: crop simulation, maize model, potential productivity, water stress, spring maize Introduction of relationships between potential maize yields and en- vironmental factors have shown that light, temperature Maize (Zea mays L.) has become a major crop on and water availability are crucial yield determinants the Loess Plateau, China, in the last ten years, and now (Cirilo and Andrade, 1994). covers ca. 27.3 of the agricultural area in the region (Xue Several authors have attempted to quantify yield et al., 2008). The increase of its cultivation has been potential and its variation at a regional scale using both prompted by agricultural advances, such as improve- observed data (Duncan et al., 1973) and modeling (Mu- ments in crop rotations in conjunction with improve- chow et al., 1990; Löffler et al., 2005; Tojo Soler et al., ments in uses of agricultural equipment and human re- 2007; Grassini et al., 2009). The models used, e.g. CROP- sources, and increases in maize prices (Xue et al., 2008). GRO and CERES have deficiencies in simulating maize However, the area is mostly located in a semiarid region production (Sadler et al., 2000), despite continuing at- of China, where the annual precipitation ranges from tempts to improve them and extend their range of ap- 150–300 mm in the north to 500–700 mm in the south plicability (Pedersen et al., 2004; Sau et al., 2004; Lizaso (Li and Xiao, 1992), and water availability for crop pro- et al., 2001). However, in all of the cited studies, yield of duction is often sub-optimal due to both overall shortag- maize crops have been evaluated using mean values of es and uneven distributions of water supplies during the meteorological variables for full growing seasons, rather year. Hence, drought has long been the primary factor than during specific growth phases when crops are most limiting crop production in the area (Kang et al., 2002; strongly affected by environmental factors. In addition, Wang et al., 2009; Zhang et al., 2009). However, there is the applied practices may not have been optimal for a lack of robust information regarding effects of meteo- maximizing yields at the study sites in some cases. Nev- rological variations on maize yields, although there has been ertheless, maize yields (measured and simulated) seem some investigation of their effects during the maize growing to be substantially lower than potential yields (Grassini season (Liu et al., 2010a, b). et al., 2009). Sharply rising global demands for biofuel (in con- The Hybrid-Maize model (Yang et al., 2004, 2006) junction with continuing increases in food and feed re- is a process-based model for simulating the development quirements) are expected to lead to further substantial and growth of maize with daily time steps, assuming no increases in maize production (Cassman et al., 2003), but growth limitations due to nutrient deficiency, toxicity, potential maize yields have increased little in the last diseases, insect pests or weeds. It incorporates functions three decades (Duvick and Cassman, 1999; Tollenaar simulating: temperature effects of maize development; and Lee, 2002). Any crop productivity is directly related photosynthesis (in vertically integrated canopy layers); to its uptake of resources, e.g. light and water, and its ef- growth-related, organ-specific respiration; and temper- ficiency of utilizing them to generate biomass (Azam-Ali ature-dependent maintenance respiration. Due to the et al., 1994; Yang et al., 2004; Liu et al., 2010a). Analyses inclusion of photosynthesis and respiration (growth and Sci. Agric. v.69, n.5, p.300-307, September/October 2012 301 Liu et al. Hybrid-Maize model analysis for maize productivity maintenance) the model may be more sensitive to varia- a single wheat or maize crop is produced per year, by tions in environmental conditions than other models, rain-fed agriculture. e.g. CERES-Maize (Jones and Kiniry, 1986; Jones et al., Two experimental treatments were applied in this 2003), and the Muchow-Sinclair-Bennett model (Mu- study. In one, designated “rain-fed”, the only water sup- chow et al., 1990), which utilize radiation-use efficiency plied to the crops was natural rainfall. In the other, desig- to integrate effects of assimilation and respiration pro- nated “irrigated”, furrow irrigation (using tap water) was cesses. Therefore, to address gaps in knowledge regard- applied to maintain the water content of the soil at 70–85 ing maize productivity and its variability, we used the % of field capacity by raising it to 85 % whenever it fell Hybrid-Maize model (Yang et al., 2004) to assess limits below 70 %. The field was irrigated in this manner with of maize total aboveground biomass (tDM) production irrigation depths, on each occasion, of 33.7 mm. These and grain yield (Gr.Y) on the Loess Plateau. The primary treatments were applied to plots measuring 7.8 m × 6.5 objectives were to: (i) evaluate the performance of the m, with four replicates. The maize used in the experi- Hybrid-Maize model for simulating maize growth, devel- ment was ‘pioneer 335’, a spring cultivar that is widely opment, and yield on the Loess Plateau; (ii) to apply the used by farmers in the region. Following a base fertilizer model to evaluate the impact of meteorological variables dose of nitrogen (110 kg ha−1), phosphorus (50 kg ha−1), on maize yield under irrigated and rain-fed conditions. and potassium (100 kg ha−1), the maize was sown in 5 cm holes spaced 20 cm apart in rows spaced 60 cm apart Materials and Methods on April 20 in both years. Water (300 mL) was poured into each seed hole before backfilling, to promote ger- Field experiment design mination and seedling emergence. Additional nitrogen, Data used to evaluate and apply the model were in the form of urea, was applied using a hole-sowing ma- acquired from field experiments at sites on flat farm- chine in the furrows at the jointing and tasseling stages, land in 2007 and 2008 at the Changwu Agriecological at rates of 80 kg N ha−1 and 90 kg N ha−1, respectively, Station (35.2° N, 107.8° E, 1,200 m above sea level), on following a nutrient management plan aimed to achieve the Loess Plateau, Shaanxi Province, China. The station a final maize yield of 14 t ha−1. is located in the Changwu tableland-gully region on the southern part of the Loess Plateau, northwest China Sampling and measurements (Figure 1). The average annual precipitation from 1990 During vegetative stages, the phenological devel- to 2008 was 578 ± 69 mm, with 55 % falling between opment of the maize was monitored by: counting dai- July and September, and the annual average temperature ly leaf collars; recording silking when silks extended 9.1 ± 2.3 °C. According to the Chinese Soil Taxonomy, beyond the husks of half the plants in each plot; and the soils are Cumuli-Ustic Isohumosols, and contained determining physiological maturity, defined by “the 300 g kg–1 clay, 660 g kg–1 silt and 40 g kg–1 sand with a presence of black layers at the base of the grains” (Tojo gravimetric field capacity of 22 %, wilting point of 8 %, Soler et al., 2007), by sampling five cobs in each plot and stable water content of 15 %. The main soil physi- regularly. In addition, estimates of aboveground bio- cal and chemical properties (0-20 cm depth) of the site mass and leaf area were obtained by harvesting plants are as follows: bulk density 1.30 g cm–3, pH 8.4, organic from 1 m of the central row of each plot at the 6th leaf, matter 11.8 g kg–1, total nitrogen 0.87 g kg–1, inorganic 12th leaf, silking and milk stages, the dough stage (in nitrogen 3.15 mg kg–1, available phosphorus 14.4 mg kg–1 2008, but not 2007), and physiological maturity.
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