Browsing by Subject "Maize production"

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Adaption to rainfall and temperature variability through integration of mungbean in maize cropping
(2021) Khongdee, Nuttapon; Cadisch, Georg
Climate change has threatened global agricultural activities, particularly in tropical and subtropical regions. Rainfed cropping regions have become under more intense risk of crop yield loss and crop failure, especially in upland areas which are also prone to soil erosion. In Thailand, maize is one of the important economic crops and mostly grown in upland areas of northern regions. Maize yield productivity largely depends on the onset of seasonal rainfall. Uncertainty of seasonal rainfall adversely affects maize yield productivity. Therefore, coping strategies are urgently needed to stabilize maize yields under climate variability. In order to identify suitable coping strategies, early maize sowing and maize and mungbean relay cropping were tested on upland fields of northern Thailand. The specific aims of this thesis were (i) monitoring growth and yield performance of maize and mungbean under relay cropping, (ii) testing early maize sowing and maize – mungbean relay cropping as coping strategies under rainfall variations (Chapter 2), (iii) testing effects of relay cropping on growth and yield of mungbean under weather variability (Chapter 4), (iv) determining suitable sowing dates under erratic rainfall patterns by using a modelling approach (Chapter 3), and (v) developing a technique for diagnosis of crop water stress in maize by thermal imaging technique (Chapter 5). Specifically, in Chapter 2 early maize planting or relay cropping strategies were assessed for growth and yield performance of maize under heat and drought conditions. Maize planted in July showed, regardless of sole or relay cropping, low grain formation as a consequence of adverse weather conditions during generative growth. However, July-planted maize relay cropping produced higher above ground biomass than July-planted maize sole cropping and early planting of maize in June. Despite unfavourable weather conditions, maize was, at least partly, able to compensate for such effects when relayed cropped, achieving a higher yield compared to maize sole cropping. June-planted maize sole cropping, however, was fully able to escape such a critical phase and achieved the highest grain yield (8.5 Mg ha-1); however, its associated risk with insufficient rain after early rain spells needs to be considered. Relay cropping showed to be an alternative coping strategy to cope with extreme weather as compared to maize sole cropping. However, relay cropping reduced maize growth due to light competition at young stages of maize before mungbean was harvested (Chapter 2). This negative impact of relay cropping is partly off-set by considering of land equivalent ratio (Chapter 4). Land equivalent ratio indicated a beneficial effect of relay cropping over maize and mungbean solecropping (LER = 2.26). During high precipitation, mungbean sole cropping produced higher yield (1.3 Mg ha-1) than mungbean relay cropping (0.7 Mg ha-1). In contrast to the period of low precipitation, mungbean relay cropping used available water more efficiently and was able to establish its plant, while mungbean sole cropping could not fully withstand severe drought and heat. Mulching effects of maize residues conserved soil water which was then available for mungbean to grow under extreme weather condition. WaNuLCAS modelling approaches can be used to support the decision of maize sowing date in northern Thailand to cope with climate change as indicated by goodness of fit of the model validation (R2 = 0.83, EF = -0.61, RMSE = 0.14, ME = 0.16, CRM = 0.02 and CD = 0.56) (Chapter 3) using forty-eight-year of historical rainfall patterns of Phitsanulok province. Only 27.1% of rainfall probability was classified as a normal rainfall condition. Consequently, maize in this region had faced with high rainfall variability. From long term simulation runs, the current maize sowing date led to strong maize yield variation depending on rainfall condition. Early maize sowing i.e. 15 and 30 days before farmers and staggered planting produced higher yield than current farmers’ practice (mid of July) in most conditions (91.7%). Simulations revealed that water was the most limiting factor affecting maize growth and yield while nutrients (N and P) had only limited impact. Results of the WaNuLCAS model could be used to identify optimal maize planting date in the area prone to soil erosion and climate variation of northern Thailand; however, the model cannot fully account for heat stress. Thermal imaging technique is a useful method for diagnose maize water status. As presented in chapter 5, the developed Crop Water Stress Index (CWSI) using a new approach of wet/dry references revealed a strong relationship between CWSI and stomatal conductance (R2 = 0.82). Our study results established a linear relationship to predict final maize grain yield and CWSI values at 55 DAS as follows “Yield = -16.05×CWSI55DAS + 9.646”. Both early planting of maize and/or relay cropping with legumes are suitable coping strategies for rainfall variability prone regions. The positive response of early planting and legume relay cropping offers the opportunity of having a short-duration crop as sequential crop, providing an additional source of protein for humans and fostering crop diversification on-site. This leads to a win-win situation for farmers, food security and the environment due to an enhanced sustainability of this cropping system.
Factors influencing the adoption of agricultural machinery by Chinese maize farmers
(2021) Quan, Xiuhao; Doluschitz, Reiner
As the major labor force has shifted from rural areas to cities, labor shortages in agricultural production have resulted. In the context of technical progress impact, and depending on farm resource endowments, farmers will choose effective labor saving technology such as machinery to substitute for the missing manual labor. The reasons behind farmers’ adoption of machinery technology are worth exploring. Therefore, this study uses 4165 Chinese maize farmers as the target group. Multivariate probit models were performed to identify the factors that affect maize farmers’ adoption of four machinery technologies as well as the interrelation between these adoption decisions. The empirical results indicate that maize sowing area, arable land area, crop diversity, family labor, subsidy, technical assistance, and economies of scale have positive effects on machinery adoption, while the number of discrete fields in the farm has a negative impact. Maize farmers in the Northeast and North have higher machinery adoption odds than other regions. The adoption of these four machinery technologies are interrelated and complementary. Finally, moderate scale production, crop diversification, subsidizing agricultural machinery and its extension education, and land consolidation, are given as recommendations for promoting the adoption of agricultural machinery by Chinese maize farmers.