No abstract

Straw returning (SR) is an important means of straw utilization, which has been tested and is helpful for improving soil fertility and crop production. However, the effects of SR on plant growth and yield of paddy rice (Oryza sativa L. subsp. japonica Kato) under water-saving irrigation (WSI) are rarely investigated. In the 2015 and 2016 rice seasons, field experiments were conducted with four treatments, namely controlled irrigation with conventional fertilization (CI-CF), controlled irrigation with straw returning (CI-SR), flooding irrigation with conventional fertilization (FI-CF), and flooding irrigation with straw returning (FI-SR). The objective of the present study was to investigate the response of plant height, number of tillers, biomass, and yield to SR and irrigation management. Results indicated that SR enhanced rice yield on average by 7.9% and 7.5% and improved irrigation water use efficiency (IWUE) by 6.3% and 8.3% in 2015 and 2016, respectively. The CI-SR combination significantly increased IWUE compared with FI-CF. These results suggested that SR could offset the inhibition of rice growth caused by CI, and the CI-SR combination could be an effective measure to enhance soil fertility, maintain the rice field, and increase IWUE. Furthermore, rice growth (plant height, number of tillers, and biomass) was slightly inhibited by SR during the first 20 d of the rice season, but increased after the jointing stage (approximately 40 d after transplanting). This implied that diverting some top-dressed chemical N fertilizer into basal application may be necessary for fertilizer management to better meet crop nutrient uptakes in rice fields with SR application, especially with CI.

Major challenge in rice production is to achieve the goal of enhancing both food production and fertilizer use efficiency. Rice growth simulation model, ORYZA (v3) was used in the present study to evaluate the model under continuous flooded (CF) and alternate wetting and drying (AWD) regimes with different fertilizer nitrogen (N) rates with different N splits using a historical data of past 45 years. The model satisfactorily simulated crop biomass and nitrogen uptake at both irrigation regimes and fertilizers N rates and splits. The yield differences among the years were large due to climate change, but enhanced by N rates. The response of N curves was different at both water regimes. At 0 N rate, the slope for agronomic efficiency (AE) was high which tends to decrease with increase in N rates. With the one split basal application of N, lowest yield was found with high physiological efficiencies (PE), lowest fertilizer recoveries (RE) and lowest agronomic efficiency (AE). For both water applications and fertilizer levels, high yield with high nitrogen uptake, AE, RE and partial factor productivity (PFP) were witnessed high at four split (3:3:3:1), while having low physiological efficiency. The water productivity (irrigation + rainfall) WPI+R at basal in one N split for AWD at 150 kg N ha−1 was 1.19 kg m−3 and for CF was 0.82 kg m−3, whereas for 225 kg N ha−1 WPI+R of AWD was 1.50 kg m−3 and 1.14 kg m−3 for CF. In general, AWD exhibited high WPI+R with no rice yield penalty compared to CF. Splitting with the proper amount of fertilizer N resulted in good water productivity and nitrogen efficiencies, could lead to high rice yield.

ORYZA (v3) model was assessed by four water and nitrogen treatments for variability and uncertainty analysis in rice biomass accumulation and nitrogen assimilation simulation. It was accurate in simulating rice biomass accumulation and nitrogen assimilation with treatment specific parameters and performed relatively better under flooded irrigation with limited nitrogen conditions (FS). Variability in treatment specific calibrated parameters was low and fell within an acceptable range, with highest CV of 11.08% for stem biomass and 18.5% for leaf nitrogen content. Weakness in ORYZA (v3) was exposed when simulated by parameters from other treatments. Cross-validation errors for panicle biomass (WSO), total above-ground biomass (WAGT), amount of nitrogen in leaf (ANLV) or panicle (ANSO) were acceptable. However, WAGT accumulation for FS was identified better than others. For WSO, among all parameters datasets, it performed better for parameters of flooded irrigation with full nitrogen (FF) and FS. Similarly, FS parameter was superior to others in simulating ANLV, whereas, under limited water and nitrogen (NFS) was better for ANSO. The uncertainty index, standard deviation and range varied similarly in different treatments where FS treatment showed lower uncertainty as compared to others. Findings of the current study suggested that ORYZA (v3) model can efficiently be adapted under varying water and nitrogen limited conditions.

No abstract