Evaluating APSIM-and-DSSAT-CERES-Maize Models under Rainfed Conditions Using Zambian Rainfed Maize Cultivars

Chisanga, Charles Bwalya; Phiri, Elijah; Chinene, Vernon R. N.

doi:10.3390/nitrogen2040027

Cited by 10 publications

(19 citation statements)

References 50 publications

(82 reference statements)

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“…The study revealed that high nitrogen levels and early sowing dates combined with the high-yielding cultivar increased regional production. Chisanga et al, (2021a) also noted the significance of the interaction of sowing dates and nitrogen fertilization on maize yield and yield components in the Zambian agricultural landscapes. Sowing dates affect the accumulated temperature units and duration of radiation interception, which influences nitrogen uptake and utilization efficiency (Caviglia et al, 2014).…”

Section: Simulation Of Agricultural Management Strategiesmentioning

confidence: 99%

“…The DSSAT modelling system includes the Crop Environment Resource Synthesis (CERES-Maize) model used for simulating maize (Zea mays L.) growth and yield under various environmental conditions. The model has been used to address a range of applications, such as evaluating strategies to cope with limited weather and soil conditions , evaluating climate change impacts on crop production (Babel & Turyatunga, 2015;Jiang et al, 2021;Lin et al, 2015), optimizing agronomic practices for increased production (Mubeen et al, 2016), and assessing the performance of various cultivars and their suitability in different environmental conditions (Chisanga et al, 2021c;Feleke et al, 2021). Researchers have tested the model under various production environments such as the Tropical, the Mediterranean, and Temperate regions (Ahmad et al, 2021;Arefi et al, 2017;Banterng et al, 2010;Getachew et al, 2021;Kothari et al, 2019;Vilayvong et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…To address these uncertainties, techniques such as assimilation with external data sources have been suggested to constraint the model simulations over time (Joshi et al, 2019;Li et al, 2015). In addition, application of model ensembles has been adopted to characterize biophysical processes across agricultural landscapes (Chisanga et al, 2021b;Feleke et al, 2021). Another requirement for the DSSAT-CERES-Maize model is that investment in significant amount of experimental resources is necessary to account for the variability in various seasons (Hoogenboom et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Maize Yield Response to Agricultural Management Strategies Using the DSSAT–CERES-Maize Model in Trans Nzoia County in Kenya

Kipkulei

Bellingrath-Kimura

Lana

et al. 2022

Int. J. Plant Prod.

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Maize production in low-yielding regions is influenced by climate variability, poor soil fertility, suboptimal agronomic practices, and biotic influences, among other limitations. Therefore, the assessment of yields to various management practices is, among others, critical for advancing site-specific measures for production enhancement. In this study, we conducted a multiseason calibration and evaluation of the DSSAT–CERES-Maize model to assess the maize yield response of two common cultivars grown in Trans Nzoia County in Kenya under various agricultural strategies, such as sowing dates, nitrogen fertilization, and water management. We then applied the Mann–Kendall (MK), and Sen’s Slope Estimator (SSE) tests to establish the yield trends and magnitudes of the different strategies. The evaluated model simulated long-term yields (1984–2021) and characterized production under various weather regimes. The model performed well in simulating the growth and development of the two cultivars, as indicated by the model evaluation results. The RMSE for yield was 333 and 239 kg ha−1 for H614 and KH600-23A, respectively, representing a relative error (RRMSE) of 8.1 and 5.1%. The management strategies assessment demonstrated significant feedback on sowing dates, nitrogen fertilization, and cultivars on maize yield. The sowing date conducted in mid-February under fertilization of 100 kg of nitrogen per hectare proved to be the best strategy for enhancing grain yields in the region. Under the optimum sowing dates and fertilization rate, the average yield for cultivar KH600-23A was 7.1% higher than that for H614. The MK and SSE tests revealed a significant (p < 0.05) modest downwards trend in the yield of the H614 cultivar compared to the KH600-23A. The eastern part of Trans Nzoia County demonstrated a consistent downwards trend for the vital yield enhancement strategies. Medium to high nitrogen levels revealed positive yield trends for more extensive coverage of the study area. Based on the results, we recommend the adoption of the KH600-23A cultivar which showed stability in yields under optimum nitrogen levels. Furthermore, we recommend measures that improve soil quality and structure in the western and northern parts, given the negative model response on maize yield in these areas. Knowledge of yield enhancement strategies and their spatial responses is of utmost importance for precision agricultural initiatives and optimization of maize production in Trans Nzoia County.

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Section: Simulation Of Agricultural Management Strategiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of Maize Yield Response to Agricultural Management Strategies Using the DSSAT–CERES-Maize Model in Trans Nzoia County in Kenya

Kipkulei

Bellingrath-Kimura

Lana

et al. 2022

Int. J. Plant Prod.

Self Cite

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“…Crop simulation model genetic coefficient development and calibration are vital for establishing their credibility and ability in simulating growth, development, yield attributes and yield of crops [32]. Among the four varietal genetic coefficients (Table 3) generated during the Rabi season of 2019-20, the time between emergence to flower appearance (EM-FL) and time between the first flower to first pod (FL-SH) ranged from 17.50 to 19.00 photothermal days and 7.00 to 8.00 photothermal days, respectively.…”

Section: Genetic Coefficient Development and Calibration Of Dssat Modelmentioning

confidence: 99%

Integrating Synthetic Aperture Radar (SAR) Sentinel 1A and CROPGRO Peanut Simulation Model for Spatial Yield Gap Analysis

Thirumeninathan¹,

Pazhanivelan²,

Sudarmanian³

et al. 2023

Preprint

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Crop yield data is critical for managing sustainable agriculture and assessing national food security. Current study aims to increase Peanut productivity from current levels by analyzing the yield gap of production potential between theoretical yield and actual farmers’ yields. The spatial yield gap of Peanut for Thiruvannamalai district of Tamil Nadu is examined in this paper by integrating the products of microwave remote sensing (SAR Sentinel-1A) with DSSAT CROPGRO peanut simulation model. CROPGRO Peanut model was calibrated and validated by conducting field experiment at Oilseeds Research Station, Tindivanam during Rabi 2019 for predominant cultivars viz. TMV 7, TMV 13, VRI 2 and G 7. Actual attainable yield was recorded by organizing CCE with help of Department of Agriculture Economics and Statistics in the respective monitoring Villages. Regression analysis between maximum recorded DSSAT Leaf Area Index (LAI) at peak flowering stage of peanut and yield recorded by Crop Cutting Experiment (CCE) for spatial yield estimation of Peanut in Thiruvannamalai district of Tamil Nadu during Rabi 2021 was carried out using ArcGIS 10.6 software. The results showed that the simulated potential yield ranged from 3194 to 4843 kg/ha, whereas actual yield ranged from 1228 to 3106 kg/ha, with a considerable disparity between the actual and potential yield levels (1217 to 2346 kg/ha) of the monitored locations. The minimum, maximum and average yield gaps in Peanut for Thiruvannamalai district was assessed as 1890, 2324 and 2134 kg/ha, respectively. To reduce the production difference (Yield gap) of Peanut cultivation, farmers should focus more on management issues such as time of sowing, irrigation or water management, quantity and sources of nutrients, cultivar selection and availability of quality seeds tailored to each region.

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“…However, the simulation of wheat grain yield and grain N at application of 150kg N/ha and soil organic carbon was poor. Several researchers have reiterated that, an adequately calibrated DSSAT models is a useful decision support management tool for assessing and predicting crop yield, nitrogen uptake, and other parameters [16,[19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Climate Change Impact on Management Practices of Maize Yield: Case of Mount Makulu, Zambia

Chisanga,

Chilambwe,

Kipkulei

et al. 2024

Preprint

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Long-term rainfall, temperature and solar radiation time series data are required to simulate crop yield and yield variability. A field experiment conducted at Mount Makulu was used to simulate the interactive effect of planting dates (SD1, SD2, SD3), maize varieties (PIO30G19, PIO30B50, ZMS606), and nitrogen fertilizer application levels (N1 = 66; N2 = 132; N3 = 198 kg N ha-1) on strategic and economic assessment. Statistical downscaled climate datasets from three GCMs from 1971-2000, 2010-2039, 2040-2069, and 2070-2099 using Representative Concentration Pathways (RCP4.5, RCP8.5) were utilized as DSSAT v4.7 inputs. The Seasonal analysis Program of the DSSAT model was used to simulate the impacts of climate change on maize yield. Results show increasing trends in temperature while there is variability in rainfall. The biophysical analysis showed varied grain yield responses to sowing date, maize cultivars and N application rates. The Mean-Gini analysis showed that PIO30B50 had an efficient late sowing data (SD3) with an application of 132 and 168 kg N ha-1 under both scenarios. Further, PIO30G19 at SD3 with 198 kg N ha-1 would be the most dominant management option for maize grain yield under future climate scenarios from 2010-2099. This research emphasizes the urgency for tailored adaptation actions and collaborative efforts along the maize value chain to mitigate future yield losses and sustain food security. Increasing maize grain yield requires implementing adaptation strategies such as varying sowing dates, adopting late-maturing varieties with high thermal heat requirements under future climate scenarios.

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Evaluating APSIM-and-DSSAT-CERES-Maize Models under Rainfed Conditions Using Zambian Rainfed Maize Cultivars

Cited by 10 publications

References 50 publications

Assessment of Maize Yield Response to Agricultural Management Strategies Using the DSSAT–CERES-Maize Model in Trans Nzoia County in Kenya

Assessment of Maize Yield Response to Agricultural Management Strategies Using the DSSAT–CERES-Maize Model in Trans Nzoia County in Kenya

Integrating Synthetic Aperture Radar (SAR) Sentinel 1A and CROPGRO Peanut Simulation Model for Spatial Yield Gap Analysis

Climate Change Impact on Management Practices of Maize Yield: Case of Mount Makulu, Zambia

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