Yield response, water productivity, and seasonal water production functions for maize under deficit irrigation water management in southern Taiwan

Greaves, Geneille E.; Wang, Yumin

doi:10.1080/1343943x.2017.1365613

Cited by 46 publications

(32 citation statements)

References 36 publications

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“…In the full irrigation treatments I10 and I11, WUE is lower and produced yields that were higher, resulting in higher water use compared to double irrigation (I7). Summer maize WUE was found to increase with deficit irrigation in southern Taiwan [48]. This study is in agreement with the study of Kird [49] who stated that water productivity can be increased by reducing the number of irrigation applications.…”

Section: Seasonal Water Use Efficiencysupporting

confidence: 91%

Optimizing the Sowing Date and Irrigation Strategy to Improve Maize Yield by Using CERES (Crop Estimation through Resource and Environment Synthesis)-Maize Model

et al. 2019

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Summer maize (Zea mays L.) is a widely cultivated crop in the arid and semi-arid Guanzhong region of China. However, due to the spatial and temporal variation in rainfall, the seasonal maize yield varies substantially and occasionally is not economical for poor farmers to produce. Recent water-saving agricultural practices were developed by the government to make it possible to apply supplementary irrigation at optimum sowing dates to maximize maize production under limited rainfall in the region. CERES (Crop Estimation through Resource and Environment Synthesis)-maize model was used to identify the appropriate irrigation strategies, crop growth stages and sowing dates for sustainable maize production. Model calibration process were carried out for full irrigation treatments of four growing seasons, (2012–2015). The data used for calibration included: Crop phenology, grain yield, aboveground biomass and leaf area index. The calibration phase model showed good agreement between simulated and observed values, with normalized root mean square error (nRMSE) ranging from 4.51% to 14.5%. The performance of the calibrated model was evaluated using the field data of grain yield, aboveground biomass, leaf area index and water use efficiency. The performance of the model during evaluation was satisfactory with acceptable nRMSE error ranging from 7% to 10%. Soil moisture content was evaluated for full irrigation treatments for both 2012 and 2013 seasons. With results showing that soil moisture content below 35 cm layer was well simulated with nRMSE, 0.57 to 0.86 respectively. Appropriate simulated sowing dates for higher production and water productivity were from 14 to 24 June. The proper amount and timing of irrigation water application was 100 mm at the flowering stage, and 100 mm at the grain filling stage respectively. Summer maize yield can be improved by adjusting the sowing date and applying supplementary irrigation when precipitation cannot meet the crop water demand in the Guanzhong Plain.

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Section: Seasonal Water Use Efficiencysupporting

confidence: 91%

Optimizing the Sowing Date and Irrigation Strategy to Improve Maize Yield by Using CERES (Crop Estimation through Resource and Environment Synthesis)-Maize Model

et al. 2019

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“…Model output was analyzed regarding the relative transpiration T a /T p , an indicator of overall crop water stress, for the nine scenarios of root length density profile shapes. It is worth noting that relative transpiration is often considered proportional to relative yield [57,58].…”

Section: Sensitivity Of Agro-hydrological Model Output To Root Profile Shapementioning

confidence: 99%

Mathematical Description of Rooting Profiles of Agricultural Crops and its Effect on Transpiration Prediction by a Hydrological Model

2019

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The geometry of rooting systems is important for modeling water flows in the soil-plant-atmosphere continuum. Measured information about root density can be summarized in adjustable equations applied in hydrological models. We present such descriptive functions used to model root density distribution over depth and evaluate their quality of fit to measured crop root density profiles retrieved from the literature. An equation is presented to calculate the mean root half-distance as a function of depth from root length density profiles as used in single root models for water uptake. To assess the importance of the shape of the root length density profile in hydrological modeling, the sensitivity of actual transpiration predictions of a hydrological model to the shape of root length density profiles is analyzed using 38 years of meteorological data from Southeast Brazil. The cumulative root density distributions covering the most important agricultural crops (in terms of area) were found to be well described by the logistic function or the Gompertz function. Root length density distribution has a consistent effect on relative transpiration, hence on relative yield, but the common approach to predict transpiration reduction and irrigation requirement from soil water storage or average water content is shown to be only partially supported by simulation results.

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“…De um lado, há a necessidade de água para a cultura e os efeitos do déficit hídrico e, do outro, os rendimentos de produção da cultura (KABOOSI;KAVEH, 2012). A quantificação deste coeficiente fornece um meio eficaz de simular a produtividade da cultura em resposta ao déficit hídrico para uma determinada região (GREAVES;WANG, 2017).…”

Section: Introductionunclassified

Fator De Resposta Produtiva De Mombaça E Marandu Ao Déficit Hídrico

Mombach

Pedreira

Santos

et al. 2019

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O objetivo com este estudo foi compreender o efeito da disponibilidade hídrica sobre a produtividade no capim-mombaça [Megathyrsus maximus (Jacq.) B. K. Simon & S. W. L. Jacobs] e capim-marandu [Brachiaria brizantha (Hochst. ex A. Rich.) R. D. Webster], determinando o coeficiente de resposta ao déficit hídrico (ky). O experimento foi avaliado em delineamento de blocos ao acaso, com duas forragens e três repetições. O período experimental correspondeu a doze ciclos de crescimento com intervalos fixos de colheita (28 dias para a estação chuvosa e 42 dias para a estação seca). Os ky foram determinados com base nos dados de produção real e potencial das forrageiras, bem como nos dados de evapotranspiração real e máxima. Para ambas as forragens, o acúmulo de forragem (AF) foi maior no outono e menor no inverno (5310 e 626 kg de matéria seca ha-1, respectivamente). Na média das quatro estações, o AF para capim-marandu foi 21,3% maior que o capim-mombaça (3344 vs. 2756 kg matéria seca ha-1, respectivamente). Para o capim-mombaça, o ky foi de 1,05, indicando sensibilidade média a alta ao déficit hídrico. No entanto, o ky foi de 0,63 para o capim-marandu, o que representa baixa sensibilidade ao déficit de água do solo.Palavras-chave: Brachiaria; coeficiente de sensibilidade; Megathyrsus; tolerância a seca. YIELD RESPONSE FACTOR OF MOMBAÇA GUINEA GRASS AND MARANDU PALISADE GRASS TO WATER DEFICIT ABSTRACT: The objective with this research was to understand the effect of water availability on Mombaça guinea grass [Megathyrsus maximus (Jacq.) B. K. Simon & S. W. L. Jacobs] and Marandu palisade grass [Brachiaria brizantha (Hochst. ex A. Rich.) R. D. Webster] productivity, determining the yield response factor to water deficit (ky). The experiment was evaluated in a randomized block design with two forages and three replicates. The experimental period corresponded to twelve growth cycles with fixed harvest intervals (28 days for the rainy season and 42 days for the dry season). The ky were determinate based on the data of real and potential yield of grasses, as well as the data of real and maximum evapotranspiration. To both grasses, herbage accumulation (HA) was greatest in the autumn and leats in the winter (5310 and 626 kg dry matter ha-1, respectively). On average of the four seasons, the HA for Marandu palisade grass was 21.3% higher than Mombaça guinea grass (3344 vs 2756 kg dry matter ha-1, respectively). For Mombaça guinea grass, ky was 1.05, indicating medium to high sensitivity to water deficit. However, the ky was 0.63 for Marandu palisade grass, which represents low sensitivity to soil water deficit.Keywords: Brachiaria; drought tolerance; Megathyrsus; sensitivity coefficient.

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Yield response, water productivity, and seasonal water production functions for maize under deficit irrigation water management in southern Taiwan

Cited by 46 publications

References 36 publications

Optimizing the Sowing Date and Irrigation Strategy to Improve Maize Yield by Using CERES (Crop Estimation through Resource and Environment Synthesis)-Maize Model

Optimizing the Sowing Date and Irrigation Strategy to Improve Maize Yield by Using CERES (Crop Estimation through Resource and Environment Synthesis)-Maize Model

Mathematical Description of Rooting Profiles of Agricultural Crops and its Effect on Transpiration Prediction by a Hydrological Model

Fator De Resposta Produtiva De Mombaça E Marandu Ao Déficit Hídrico

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