Forgoing the fallow in Bangladesh's stress-prone coastal deltaic environments: Effect of sowing date, nitrogen, and genotype on wheat yield in farmers’ fields

2015 ◽  
Vol 170 ◽  
pp. 7-20 ◽  
Author(s):  
Timothy J. Krupnik ◽  
Zia Uddin Ahmed ◽  
Jagadish Timsina ◽  
Md. Shahjahan ◽  
A.S.M. Alanuzzaman Kurishi ◽  
...  
Keyword(s):  
Wheat Yield ◽  
Sowing Date

scholarly journals Identification of the most appropriate adaptation for rice and wheat in the face of climate change in eastern India

2021 ◽  
Author(s):  
Madhuri Dubey ◽  
Ashok Mishra ◽  
Rajendra Singh
Keyword(s):  
Climate Change ◽  
Food Security ◽  
Crop Yield ◽  
Negative Impact ◽  
Wheat Yield ◽  
Sowing Date ◽  
Irrigation Scheduling ◽  
Fertilizer Management ◽  
Eastern India ◽  
Adaptation Options

Abstract The changing climate affects natural resources that impart a negative impact on crop yield and food security. It is thus imperative to identify agro-climate wise, area-specific adaptation options to ensure food security. This study, therefore, evaluated some feasible adaptation options for two staple food grain crops, rice and wheat, in different agro-climatic regions (ACRs) of Eastern India. Alteration in transplanting date, seedling age, and fertilizer management (rate and split of fertilizer) for rice; and sowing date, fertilizer management, and deficit irrigation scheduling for wheat, are assessed as adaptation options. Crop environment and resource synthesis (DSSAT) model is used to simulate the crop yield using different plausible adaptation options to projected climate scenarios. Findings show that shifting transplanting/sowing date, and nitrogen fertilizer application at 120% of recommended nitrogen dose with four splits could be an effective adaptation for rice and wheat crops. Results also emphasize that transplanting of 18 days older seedlings may be beneficial in rice cultivation. In contrast, irrigation at a 30–40% deficit of maximum available water would sustain the wheat yield under climate change conditions. This study suggests the best combination of adaptation options under climate change conditions in diverse ACRs, which may assist agriculturists in coping with climate change.

Factors influencing the soil-test calibration for Colwell P and wheat under winter-dominant rainfall

2020 ◽  
Vol 71 (2) ◽  
pp. 113
Author(s):  
Mark Conyers ◽  
Richard Bell ◽  
Michael Bell
Keyword(s):  
Soil Type ◽  
Wheat Yield ◽  
Sowing Date ◽  
Soil Test ◽  
Soil Tests ◽  
Factors Affecting ◽  
Soil P ◽  
Critical Range ◽  
South Wales ◽  
Dry Conditions

Critical ranges for soil tests are based on results that inevitably involve some broad variance around the fitted relationship. Some of the variation is related to field-based factors affecting crop response to nutrients in the soil and some to the efficiency of the soil-test extractant itself. Most attempts to improve soil tests focus on the extractant, whereas here, we explore the variation that could be accounted for by field-based factors in the soil-test calibration relationship between Colwell phosphorus (P) and wheat yield, using the Australian Better Fertiliser Decisions for Crops database—the biggest dataset available for this relationship. Calibrations developed from this dataset have been criticised, and so we aimed to explore factors accounting for more of the variation in the relationships for the dryland, winter-dominant rainfall region of southern New South Wales. As reported previously, soil type was shown to influence the critical range and r-value for the Colwell P soil-test calibration for P responses by wheat. We also identified a tendency for dry conditions, at sowing or during the season, to lower relative yields for a given soil-test value, indicating increased reliance on fertiliser P over soil P. A similar trend was evident for later sowing date, again suggesting an increased probability of crop P requirements being met from the fertiliser P. However, additional records need to be generated to establish definitively that early sowing or subsurface P reserves minimise response to fertiliser P. In general, factors that influence crop access to soil P will have an impact on response to fertiliser P. Although this analysis shows that it is possible to ‘tighten’ the response curve for Colwell P and wheat by restricting the data for a given soil type to ideal management and seasonal conditions, the ‘outliers’ that are excluded frequently reflect an important subset of environmental conditions encountered by wheat crops in dryland agriculture.

scholarly journals Optimizing the Sowing Date to Improve Water Management and Wheat Yield in a Large Irrigation Scheme, through a Remote Sensing and an Evolution Strategy-Based Approach

2021 ◽  
Vol 13 (18) ◽  
pp. 3789
Author(s):  
Salwa Belaqziz ◽  
Saïd Khabba ◽  
Mohamed Hakim Kharrou ◽  
El Houssaine Bouras ◽  
Salah Er-Raki ◽  
...  
Keyword(s):  
Irrigation Water ◽  
Wheat Yield ◽  
Agricultural Practices ◽  
Sowing Date ◽  
Irrigation Scheduling ◽  
Evolution Strategy ◽  
Wheat Crop ◽  
Irrigation Scheme ◽  
Sowing Dates ◽  
Large Irrigation

This study aims to investigate the effects of an optimized sowing calendar for wheat over a surface irrigation scheme in the semi-arid region of Haouz (Morocco) on irrigation water requirements, crop growth and development and on yield. For that, a scenario-based simulation approach based on the covariance matrix adaptation–evolution strategy (CMA-ES) was proposed to optimize both the spatiotemporal distribution of sowing dates and the irrigation schedules, and then evaluate wheat crop using the 2011–2012 growing season dataset. Six sowing scenarios were simulated and compared to identify the most optimal spatiotemporal sowing calendar. The obtained results showed that with reference to the existing sowing patterns, early sowing of wheat leads to higher yields compared to late sowing (from 7.40 to 5.32 t/ha). Compared with actual conditions in the study area, the spatial heterogeneity is highly reduced, which increased equity between farmers. The results also showed that the proportion of plots irrigated in time can be increased (from 40% to 82%) compared to both the actual irrigation schedules and to previous results of irrigation optimization, which did not take into consideration sowing dates optimization. Furthermore, considerable reduction of more than 40% of applied irrigation water can be achieved by optimizing sowing dates. Thus, the proposed approach in this study is relevant for irrigation managers and farmers since it provides an insight on the consequences of their agricultural practices regarding the wheat sowing calendar and irrigation scheduling and can be implemented to recommend the best practices to adopt.

scholarly journals Contribution of main culm and tillers to grain yield of durum wheat: Influence of sowing date and plant traits

2018 ◽  
pp. 235-247 ◽  
Author(s):  
Iduna Arduini ◽  
Elisa Pellegrino ◽  
Laura Ercoli
Keyword(s):  
Grain Yield ◽  
Durum Wheat ◽  
Plant Traits ◽  
Stem Elongation ◽  
Wheat Yield ◽  
Sowing Date ◽  
Early Spring ◽  
Tiller Number ◽  
Yield Stability ◽  
Initiation Rate

The question of whether tillers are a burden or a resource in durum wheat is of concern in the variable Mediterranean climates. The contribution of tillers to grain yield was investigated in commercial cultivars differing in time to anthesis, tillering and spike size, in response to three sowing dates:mid-autumn (recommended), winter, and early spring. The thermal time of phenological phases was calculated, and yield-components and floret production were analysed separately in main culm and tillers. Tiller spikes showed higher spikelet abortion coupled to lower spikelet fertility and mean kernel weight, so that grain yield was 40-60% lower than in main culm spikes. Despite this, tillers contributed 35 to 50% to plant yield. The sowing date affected tiller number rather than one tiller yield. In winter sowings (December), lower main culm yield was fully compensated by increased tiller yield, whereas shifts of sowing date to early spring (February) reduced tillering, which caused a yield loss ranging from 12 to 20%. Cultivars differed in one tiller yield rather than in tiller number, and higher grain yield of tillers was primarily due to increased grain recovery. A more equal partitioning of resources within main culm and tillers corresponded to better yield stability across sowing dates. Starting from this, we suggest that early anthesis, a long stem elongation phase, a high primordium initiation-rate and small spikes, could be positive traits for durum wheat yield stability in changing environments, since they allow plants directing more time and resources to floret production and grain filling both in main culm and tiller spikes. From a methodological point of view, our results show that the number of fertile florets per spike is highly correlated with the average floret number of five given spikelets.

scholarly journals The Potential of Landsat NDVI Sequences to Explain Wheat Yield Variation in Fields in Western Australia

2021 ◽  
Vol 13 (11) ◽  
pp. 2202
Author(s):  
Jianxiu Shen ◽  
Fiona H. Evans
Keyword(s):  
Crop Yield ◽  
Western Australia ◽  
Curve Fitting ◽  
Wheat Yield ◽  
Growth Curves ◽  
Sowing Date ◽  
Estimation Accuracy ◽  
Yield Estimation ◽  
Additional Information ◽  
Yield Variation

Long-term maps of within-field crop yield can help farmers understand how yield varies in time and space and optimise crop management. This study investigates the use of Landsat NDVI sequences for estimating wheat yields in fields in Western Australia (WA). By fitting statistical crop growth curves, identifying the timing and intensity of phenological events, the best single integrated NDVI metric in any year was used to estimate yield. The hypotheses were that: (1) yield estimation could be improved by incorporating additional information about sowing date or break of season in statistical curve fitting for phenology detection; (2) the integrated NDVI metrics derived from phenology detection can estimate yield with greater accuracy than the observed NDVI values at one or two time points only. We tested the hypotheses using one field (~235 ha) in the WA grain belt for training and another field (~143 ha) for testing. Integrated NDVI metrics were obtained using: (1) traditional curve fitting (SPD); (2) curve fitting that incorporates sowing date information (+SD); and (3) curve fitting that incorporates rainfall-based break of season information (+BOS). Yield estimation accuracy using integrated NDVI metrics was further compared to the results using a scalable crop yield mapper (SCYM) model. We found that: (1) relationships between integrated NDVI metrics using the three curve fitting models and yield varied from year to year; (2) overall, +SD marginally improved yield estimation (r = 0.81, RMSE = 0.56 tonnes/ha compared to r = 0.80, RMSE = 0.61 tonnes/ha using SPD), but +BOS did not show obvious improvement (r = 0.80, RMSE = 0.60 tonnes/ha); (3) use of integrated NDVI metrics was more accurate than SCYM (r = 0.70, RMSE = 0.62 tonnes/ha) on average and had higher spatial and yearly consistency with actual yield than using SCYM model. We conclude that sequences of Landsat NDVI have the potential for estimation of wheat yield variation in fields in WA but they need to be combined with additional sources of data to distinguish different relationships between integrated NDVI metrics and yield in different years and locations.

scholarly journals Enhancing rainfed wheat productivity through technology validation and dissemination in Nertiti locality of Central Darfur State-Sudan

2021 ◽  
Vol 9 (3) ◽  
pp. 281-287
Author(s):  
Elkhalil Elnour Breima ◽  
Amani Ahmed Idris ◽  
Elhafiz Abdalla Haroon ◽  
Aboh Adam Elnour ◽  
Ali Ahmed Dawoud
Keyword(s):  
Wheat Variety ◽  
Wheat Yield ◽  
Sampling Technique ◽  
Sowing Date ◽  
Crop Productivity ◽  
Farm Size ◽  
Research Station ◽  
Land Preparation ◽  
Pests And Diseases ◽  
Net Returns

This research has been conducted in Nertiti locality of central Darfur State during 2020/2021 cropping season in order to enhance rainy wheat productivity through technology validation and dissemination. Farmers Field School was established with 25 farmers (5 men and 20 women). On-station demonstration farm was conducted with an area of 2feddan. Improved wheat variety Zakia (1feddan) grown versus Nillin (0.5 feddan) and local (0.5 feddan) to evaluate crop performance and potentiality. Clustered random sampling technique applied. Evaluation questionnaires developed randomly to 20 farmers to know farmers perceptions on wheat performance and productivity. Farmers subjected to farming training in wheat technologies of land preparation, sowing date, seed preparation, seed dressing, weeding, water harvesting, soil conservation, pests and diseases control, seed production technologies and harvest and post harvest technologies. Results showed that excessive rainfall variation during reproductive growth affect wheat yield by 32%. Socioeconomic characteristics and frequency distribution of farmers revealed that 80% of FFS participants were educated and 20% illiterate. 10% of farmers have farm size less than 1 feddan, 50% have 1-2 feddan, 30% have farm size range between 3-4 feddan and 10% have more than 4 feddan. Agriculture is the main source of income (80%), while 20% traders and pity trading. Results noted that 90% of FFS participants have no extension services and only 10% have.40% of respondent’s have experience in wheat production, while 60 have no experience.40% of participants perceived that improved wheat is resistant to water logging and high rain fall, 20% resist to insects, 30% high yielded and 10% said low yielded. Crop productivity trend showed that highest grain yield was obtained by improved Zakaia (655 kg/feddan) followed by Nillin (626 kg/feddan), while the lowest yield gained by local (424 kg/fedan). It was also recorded that improved Zakia exceed local and Nillin yields by 54% and 5%, respectively. Partial crop budget result revealed that all treatments were financially gave positive net returns. Improved Zakia gave highest net returns (SDG 21,500), Nillin (SDG18, 900) and the lowest net returns obtained by local wheat (SDG 15,000). Study recommended training in wheat technologies and enhancing the role of research station in the study area.

scholarly journals Wheat varieties productivity at different sowing dates

2020 ◽  
Vol 26 (2) ◽  
pp. 61-68
Author(s):  
Milica Veliborović ◽  
Ljubiša Kolarić ◽  
Ljubiša Živanović ◽  
Jela Ikanović
Keyword(s):  
Yield Components ◽  
Wheat Yield ◽  
Sowing Date ◽  
Grain Weight ◽  
High Yield ◽  
Sowing Time ◽  
Wheat Varieties ◽  
Sowing Dates ◽  
Two Factors ◽  
Set Up

The aim of this research is to study two important factors in wheat production (Triticum sp.) - sowing time and variety, specifically their influence on wheat yield and yield components: the number of grains per spike, grain weight per spike, 1,000-grain weight, and the hectoliter weight. The influence of sowing time and variety were determined based on the obtained data, which indicated the extent to which the significance of these factors manifest on certain components and wheat yield. The optimal sowing date is October 5th-25th. The variety itself cannot give a high yield without the application of all agrotechnical measures, to which we strive for primarily due to the high wheat importance. The experiment set up by the split-plot method in three replications examined three wheat varieties: Gabrio, Rapsodija and Basmati sown in two sowing dates: October 29th and November 25th. The results showed that all yield components (the number of grains per spike, the grain weight per spike, the 1,000 - grain weight, and the hectoliter weight) had the highest values in Rapsodija variety and were more dependent on the variety than to sowing time. The influence of the variety on the hectoliter mass was at a significant level. Wheat yield depended more on sowing time, and the influence of sowing time, variety, and interaction of these two factors on yield were statistically significant. The highest yield was registered in Gabrio variety (8.87 t ha-1), while the average yield achieved by this micro experiment was 8.46 t ha-1.

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