查看更多>>摘要：Maize (Zea mays L.) yield and yield variation are both affected by climate and cultivation management. Changing the sowing date (SD) is one of the most commonly used cultivation managements for achieving high yield of maize in North China Plain (NCP). Climate is one of the most important factors in maize yield variation under different SDs. But the yield variation under different SDs and the contribution of each climatic variable remain unclear. In this study, a 7-year field experiment of SDs was used to assess the changing trends of climatic variables under different SDs, and the relative contribution of climatic variables on yield and yield variation of maize were also evaluated. The experiment was conducted at Wuqiao Experimental Station in NCP, with 35 SDs in 7 years from 2013 to 2018, and 2021, totally. Through analyzing the historical meteorological data, our results showed that more photosynthetically active radiation (PAR) distributed in August and September, minimum temperature was increased from April to September, and high temperature days (HTDs) in a year was significantly advanced from 1990 to 2021. These changes in climate caused the optimum SD with both high yield and yield stability was in early-to mid-June, mainly because of the reduction of HTDs in 5 d pre-silking to 5 d post-silking (SS) and increased PAR in SS and silking to harvest (SH). Through variance partitioning analysis, the climatic variables in SS, SH and the whole growth stage (WS) contributed 56 %, 44 %, and 18 % of maize yield variation, respectively. In SS, 7 %, 28 % and 5 % of maize yield variation were explained by HTDs, PAR, and temperature independently. And this contribution was 18 % and 15 % of the PAR and temperature in SH. While in WS, only temperature explained 20 % of the yield variation. Our results highlight the importance of focusing on the yield stability of maize, and for the first time to clarify the differences in maize yield stability under different SDs in NCP. Meanwhile, the relative contribution of climate on yield variation was quantified. This study also proposed and predicted the optimal SDs for high and stable maize yield in NCP. These results will help to study the regional maize production under climate change in the future.
Grotelueschen, KristinaGaydon, Donald S.Senthilkumar, KalimuthuLangensiepen, Matthias...
14页
查看更多>>摘要：In East Africa, rainfed lowland rice is primarily produced by smallholders in alluvial floodplain and inland valley wetlands. These wetlands differ in their dominant soil types and water regimes that vary seasonally, inter-annually and between field positions. Yield responses to mineral nitrogen (N) fertiliser thus likely vary between and within wetlands and years, modulating the profitability of N fertiliser use. Therefore, the locally-validated APSIM model was used to study yield responses to N fertiliser rates (0, 30, 60, 90, 120, and 150 kg ha(-1)) and supplemental irrigation at different field positions in a floodplain in Tanzania (fringe and middle positions) and an inland valley in Uganda (valley-fringe, mid-valley and valley-bottom positions) over 30-years. Average rainfed yield gains with mineral N and N use efficiencies were high, ranging between 1.7 and 4.5 Mg ha(-1) and 27-70 kg kg(-1) in the floodplain and between 1.0 and 3.2 Mg ha(-1) and 18-34 kg kg(-1) in the inland valley, depending on field position, N rate and year. Consequently, N fertiliser use was generally profitable in both wetlands, with value/cost ratios >= 4 and marginal rates of returns > 150%. Profitable N rates in all years were 30-120 kg ha(-1) in the fringe and 30-90 kg ha(-1) in the middle positions of the floodplain, and 60-150 kg ha(-1) in the mid-valley and 90-150 kg ha(-1) in the valley-bottom positions of the inland valley. In the valley-fringe position, N fertiliser use was comparatively riskier and profitable only in 77-90% of years at N rates of 60-150 kg ha(-1). Supplemental irrigation may help boost N fertiliser use efficiencies and use profitability with average yield gains of > 1.5 and > 0.4 Mg ha(-1) in the floodplain and inland valley, respectively, while simulated spatial-temporal water stress pattern may help guide efficient irrigation scheduling.
查看更多>>摘要：Despite decades of international research and development efforts focusing on increased rice production in Africa, there is large yield gap, and the local production still needs to be complemented by rice imports to meet consumption demands. This paper aims to provide an overview of published research findings on rice yield gaps and the effects of 'good agricultural practices' (GAPs) on rice yield and nutrient use efficiency. The majority of previous studies were from irrigated lowlands, and quantified rice yield gaps as farmer-based (difference between 'actual yield' and 'best farmers' yield') and model-based yield gaps (difference between 'actual yield' and 'potential yield for irrigated and water-limited potential yield for rainfed rice'). The mean farmer- and model-based yield gaps were 3.1 and 5.0 t ha(-1) for irrigated lowland (IL); 3.1 and 7.7 t ha(-1) for rainfed lowland (RL); 2.0 and 6.0 t ha(-1) for rainfed upland (RU), respectively in Africa. An analysis of studies from literature on GAPs in Africa revealed that most studies (64 %) were from IL and a wide range of yield increases following individual components of GAPs across environments. A median yield increase of 1.0 t ha(-1) was achieved with improved water conservation practices in IL, whereas improved weed management increased yields by 0.7 t ha(-1), mainly in IL compared to farmers' practices. Application of inorganic fertilizers and/or organic amendments increased the yield by 0.8-1.2 t ha-1 across the environments. Integration of GAPs increased the median yield by 2.1 and 1.5 t ha(-1) in IL and RL, respectively. The calculated agronomic efficiency of N, P and K from fertilizer experiments were within the desirable levels mainly in the IL and RL environments and comparable to the values from similar environments in Asia. For instance, the agronomic efficiency of N was 21, 13 and 12 kg kg(-1), respectively for IL, RL and RU in Africa. Although rice yield gaps in Africa can be substantially reduced by introduction of integrated GAPs to farmers, there is large difference between model-based yield gaps and yield gain obtained by integrated GAPs. Further efforts are needed to identify factors causing this difference. We recommend a research and development focus on rainfed lowland rice systems, which have the largest model-based yield gaps by partially converting them to irrigated systems, and on improving nutrient use efficiencies and closing nutrient cycles.
NSTL
Elsevier