Assessment of maize (Zea mays L.) productivity and yield gap analysis using simulation modelling in subtropical climate of central India

Mohanty, M.; Sinha, Nishant K.; Patidar, Rohit Kumar; Somasundaram, J.; Chaudhary, RS; Hati, K. M.; Reddy, K Sammi; Prabhakar, M.; Rao, Srinivas C.

doi:10.54386/jam.v19i4.603

Cited by 9 publications

(3 citation statements)

References 2 publications

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“…The MAE (10.3), MBE (10.3) and RMSE (11.03) (Table 2) analysis also supported that the model slightly overestimated all the cases value. Mohanty et al, (2017) also found the model to overestimate days to physiological maturity stage in maize. The lower maximum temperature during tasseling to dough stage and higher solar radiation at silk emergence to physiological maturity could be favorable parameters for better grain yields under second date of sowing (Singh et al, 2013).…”

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confidence: 86%

Assessment of maize (Zea mays L.) productivity using CERES-maize model in South Alluvial Plain Zone of Bihar, India

Singh¹,

Kumar²,

SINGH³

et al. 2022

J. Agrometeorol.

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Maize (Zea mays L.) is cultivated widely throughout the world and has the highest production of all the cereals. Worldwide, production of maize was more than 844.36 MT (FAO, 2017) and productivity of 5.22 t ha -1 . It is an important staple food in many countries and also used as animal feed and has many industrial applications. Owing to its tremendous genetic variability, the maize crop thrives well in a wide range of environments from tropical to temperate climate (Anon., 2014). Maize is grown in climates ranging from temperate to tropical during the periods when mean daily temperatures are above 15°C and frost-free. The productivity of maize in India has been reported to 1566 kg ha -1 during 2015-16 which has doubled since 2006 (DES, 2015-16).

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confidence: 86%

Assessment of maize (Zea mays L.) productivity using CERES-maize model in South Alluvial Plain Zone of Bihar, India

Singh¹,

Kumar²,

SINGH³

et al. 2022

J. Agrometeorol.

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“…As the most important animal fodder crop with the largest global production among cereals in the world, maize certainly has been attracting scholarly attention for improving its potential yield. A well parameterized and validated Agricultural Production Systems IMulator (APSIM) model was used for assessing the productivity and yield gap of maize in Madhya Pradesh of India [18], the Food and Agriculture Organization (FAO) AquaCrop model was evaluated for its predictability potential of maize growth and yields in Uganda [19], and the AquaCrop and AgroC models were calibrated and validated by using the data sets from 2015 (cool/dry season) and 2016 (warm/wet) respectively to investigate maize development and suitability in cool climate in Lithuanian of USA [20]. The AquaCrop model was evaluated relatively to maize growth, yield, and water use parameters/variables under different water stress conditions over six years (2005)(2006)(2007)(2008)(2009)(2010) in Nebraska of USA [21], while the Environmental Policy Integrated Climate (EPIC) model was assessed for its potential to simulate maize yield using limited data from field trials on maize in the eastern Cape of South Africa [22].…”

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confidence: 99%

How does climate change affect potential yields of four staple grain crops worldwide by 2030?

Cai,

LV,

WEI

et al. 2024

PLoS ONE

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Global food security basically depends on potential yields of staple grain crops worldwide, especially under climate change. However, most scholars use various models of production function in which climatic factors are often considered to estimate crop yield mostly at local or regional level. Therefore, in this paper: Potential yields of rice, wheat, maize and soybean worldwide by 2030 are projected creatively using Auto-regressive Integrated Moving Average and Trend Regressed (ARIMA-TR) model in which actual yields in recent two years are used for testing the reliability of projection and Gray System (GS) model for validating the test; Especially individual impacts of climate change on the productions of rice, wheat, maize and soybean worldwide since 1961 are analyzed by using unary regression model in which global mean temperature and land precipitation are independent variable while the yield of crop being dependent one, respectively. Results show that: by 2030, the ratio between average and top yields of world rice is projected to be 50.6% increasing, while those of world wheat, world maize and world soybean are projected to be 38.0% increasing, 14.7% decreasing and 72.5% increasing, respectively. Since 1961 global warming has exerted a negative impact on average yield of world rice less than on its top, a positive effect on average yield of world wheat while a negative impact on its top, a positive effect on average yield of world maize less than on its top, and a positive influence on average yield of world soybean while a negative one on its top, which might be slightly mitigated by ‘Carbon Peak’ target. The fluctuation of global rainfall contributes to the productions of these crops much less than global warming during same period. Our findings indicate that: to improve global production of four staple grain crops by 2030, the priorities of input should be given to either rice or wheat in both high and low yield countries, whereas to maize in high yield countries and to soybean in low yield countries. These insights highlight some difference from previous studies, and provide academia with innovative comprehension and policy-decision makers with supportive information on sustainable production of these four staple grain crops for global food security under climate change in the future.

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“…Recent studies on estimating potential yield of maize through modelling have provided a number of important insights as follows. Agricultural Production Systems IMulator (APSIM) model was parameterized and validated to assess the productivity and yield gap of maize in Madhya Pradesh of India (Mohanty et al, 2017), while the APSIM crop model was applied for investigating the interaction between sowing date and cultivar in Khuzestan province of southwestern Iran (Rahimi-Moghaddam et al, 2018). An integrated crop simulation model-satellite imagery method was used for determining the maize yield gap in four major watersheds in Golestan Province of Iran (Pourhadian et al, 2019), while the DSSAT and data assimilation scheme (DSSAT-DA) was used for estimating maize yield and evaluating the sensitivity of maize yield to hydro-climatic variables (Liu et al, 2019), and three crop simulation models (AEZ-FAO, DSSAT-CERES-Maize and APSIM-Maize) were calibrated and evaluated to estimate maize potential and attainable yields in Brazil and to assess the performance of different ensemble strategies to reduce their uncertainties for maize yield prediction (Duarte and Sentelhas, 2019).…”

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confidence: 99%

Potential yield of world maize under global warming based on ARIMA-TR model

CAI,

DENG,

CAO

2024

J. Agrometeorol.

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With continuous increase of population and demand for nutritional food, analyzing potential yield of world maize affected by global warming is of great significance to direct the crop production in the future. Thus, in this paper both average and top (national) yields of world maize between 2021 and 2030 are projected creatively using ARIMA-TR (Auto-regressive Integrated Moving Average and Trend Regression) model based on historic yields since 1961. The impact of global warming on the yields of world maize from 1961 to 2020 was analyzed using unary regression model. Our study concludes that between 2021 and 2030, average yield of world maize is projected to be from 5989 kg ha-1 to 6703 kg ha-1 while the top yield from 36530 kg ha-1 to 44271 kg ha-1, or the average ranging from 16.39% decreasingly to 15.14% of the top; from 1961 to 2020 global warming exerts positive effect on average yield of world maize less than on the top, which partly drives the gap between these two yields widened gradually; for world maize by 2030, the opportunities for improving global production should be mainly dependent on the advantage of high-yield countries.

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Assessment of maize (Zea mays L.) productivity and yield gap analysis using simulation modelling in subtropical climate of central India

Cited by 9 publications

References 2 publications

Assessment of maize (Zea mays L.) productivity using CERES-maize model in South Alluvial Plain Zone of Bihar, India

Assessment of maize (Zea mays L.) productivity using CERES-maize model in South Alluvial Plain Zone of Bihar, India

How does climate change affect potential yields of four staple grain crops worldwide by 2030?

Potential yield of world maize under global warming based on ARIMA-TR model

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