Best management systems for intensifying a maize – soybean rotation: integrating field production, plant physiology, and modeling

Doctor of Philosophy === Department of Agronomy === Ignacio Ciampitti === Potential yield (PY) is defined by the yield limited by temperature, radiation, and genetics – under no limitation on nutrients or water. The difference between PY and actual yield (AY) is defined as yield gap (YG). Management...

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Main Author: Balboa, Guillermo
Language:en_US
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/2097/38811
id ndltd-KSU-oai-krex.k-state.edu-2097-38811
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spelling ndltd-KSU-oai-krex.k-state.edu-2097-388112018-04-22T03:37:19Z Best management systems for intensifying a maize – soybean rotation: integrating field production, plant physiology, and modeling Balboa, Guillermo management systems soybean maize modeling nitrogen fixation ecological intensification Doctor of Philosophy Department of Agronomy Ignacio Ciampitti Potential yield (PY) is defined by the yield limited by temperature, radiation, and genetics – under no limitation on nutrients or water. The difference between PY and actual yield (AY) is defined as yield gap (YG). Management practices such as planting date, row spacing, seeding rate, fertilization program, pest, and disease control can help producers to intensify the productivity of the farming systems and consequently, close the YGs. To evaluate the impact of different management system (MS, specific combination of management practices) on closing the YG the following objectives were established: i) conduct a historical synthesis analysis to characterize shifts in soybean yields, biomass and nutrient uptake and partitioning dissecting the main physiological component related to nutrient use efficiency, seed nutrient composition and nutrient stoichiometry; ii) study the contribution of five MS for intensifying maize-soybean production systems; iii) quantify the nitrogen (N) contribution from the biological N fixation (BNF) process for soybeans under two contrasting MSs (low vs. high inputs); and iv) utilize the same contrasting input treatments to calibrate the Agricultural Production System Simulator (APSIM) for modeling a maize – soybean rotation and apply the parametrized model to estimate a long-term (1980-2016) simulation. For the first objective, main findings indicate that soybean yield increase over time was driven by an increase in biomass with a relatively small variation in harvest index, and with modern varieties producing more yield per unit of N uptake. For the second objective, field experiments demonstrated that intensification practices (narrow row spacing, increasing seeding rate and implementation of a balanced nutrition program) increased yields in both soybeans and maize under rainfed and irrigated conditions. For the third objective, to better understand the soybean N status, BNF measurements were collected during the 2015 growing season and also investigated in a greenhouse setting. The B value, N fixation when plants are fully relying on atmospheric N, changed among varieties, growth stages and plant fractions. Overall B value at R7 (beginning of maturity) was -1.97 contrasting with the -1.70 value reported as mode according to a literature review. For the range of fixation measured in this research (average of 45-57%), utilization of a B value obtained from the scientific literature or measured in field conditions will have a reduced impact on BNF estimations. Lastly, for the last and fourth objective, the APSIM performed well in estimating yield, biomass production and total N uptake with a high model efficiency and low relative root mean square error (RRMSE). The long-term simulation helped characterize the YG for each crop and MS according to different weather patterns. The modeling approach increased the value of data collected in field experiments. Overall, this research project provided an approach to quantifying and understanding YGs in a maize-soybean rotation and the impact of different MSs on intensifying productivity. Future work can be conducted to model specific MSs to advise producers on the best management systems (BMSs) for sustainably intensifying productivity while minimizing the environmental footprint of current farming systems. 2018-04-19T14:48:30Z 2018-04-19T14:48:30Z 2018 May Dissertation http://hdl.handle.net/2097/38811 en_US
collection NDLTD
language en_US
sources NDLTD
topic management systems
soybean
maize
modeling
nitrogen fixation
ecological intensification
spellingShingle management systems
soybean
maize
modeling
nitrogen fixation
ecological intensification
Balboa, Guillermo
Best management systems for intensifying a maize – soybean rotation: integrating field production, plant physiology, and modeling
description Doctor of Philosophy === Department of Agronomy === Ignacio Ciampitti === Potential yield (PY) is defined by the yield limited by temperature, radiation, and genetics – under no limitation on nutrients or water. The difference between PY and actual yield (AY) is defined as yield gap (YG). Management practices such as planting date, row spacing, seeding rate, fertilization program, pest, and disease control can help producers to intensify the productivity of the farming systems and consequently, close the YGs. To evaluate the impact of different management system (MS, specific combination of management practices) on closing the YG the following objectives were established: i) conduct a historical synthesis analysis to characterize shifts in soybean yields, biomass and nutrient uptake and partitioning dissecting the main physiological component related to nutrient use efficiency, seed nutrient composition and nutrient stoichiometry; ii) study the contribution of five MS for intensifying maize-soybean production systems; iii) quantify the nitrogen (N) contribution from the biological N fixation (BNF) process for soybeans under two contrasting MSs (low vs. high inputs); and iv) utilize the same contrasting input treatments to calibrate the Agricultural Production System Simulator (APSIM) for modeling a maize – soybean rotation and apply the parametrized model to estimate a long-term (1980-2016) simulation. For the first objective, main findings indicate that soybean yield increase over time was driven by an increase in biomass with a relatively small variation in harvest index, and with modern varieties producing more yield per unit of N uptake. For the second objective, field experiments demonstrated that intensification practices (narrow row spacing, increasing seeding rate and implementation of a balanced nutrition program) increased yields in both soybeans and maize under rainfed and irrigated conditions. For the third objective, to better understand the soybean N status, BNF measurements were collected during the 2015 growing season and also investigated in a greenhouse setting. The B value, N fixation when plants are fully relying on atmospheric N, changed among varieties, growth stages and plant fractions. Overall B value at R7 (beginning of maturity) was -1.97 contrasting with the -1.70 value reported as mode according to a literature review. For the range of fixation measured in this research (average of 45-57%), utilization of a B value obtained from the scientific literature or measured in field conditions will have a reduced impact on BNF estimations. Lastly, for the last and fourth objective, the APSIM performed well in estimating yield, biomass production and total N uptake with a high model efficiency and low relative root mean square error (RRMSE). The long-term simulation helped characterize the YG for each crop and MS according to different weather patterns. The modeling approach increased the value of data collected in field experiments. Overall, this research project provided an approach to quantifying and understanding YGs in a maize-soybean rotation and the impact of different MSs on intensifying productivity. Future work can be conducted to model specific MSs to advise producers on the best management systems (BMSs) for sustainably intensifying productivity while minimizing the environmental footprint of current farming systems.
author Balboa, Guillermo
author_facet Balboa, Guillermo
author_sort Balboa, Guillermo
title Best management systems for intensifying a maize – soybean rotation: integrating field production, plant physiology, and modeling
title_short Best management systems for intensifying a maize – soybean rotation: integrating field production, plant physiology, and modeling
title_full Best management systems for intensifying a maize – soybean rotation: integrating field production, plant physiology, and modeling
title_fullStr Best management systems for intensifying a maize – soybean rotation: integrating field production, plant physiology, and modeling
title_full_unstemmed Best management systems for intensifying a maize – soybean rotation: integrating field production, plant physiology, and modeling
title_sort best management systems for intensifying a maize – soybean rotation: integrating field production, plant physiology, and modeling
publishDate 2018
url http://hdl.handle.net/2097/38811
work_keys_str_mv AT balboaguillermo bestmanagementsystemsforintensifyingamaizesoybeanrotationintegratingfieldproductionplantphysiologyandmodeling
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