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A map of Australia showing transformation of peanut cultivation from dryland areas of the North and South Burnett districts to other irrigated areas in Queensland (QLD) and the Northern Territory (NT) shown by black round symbols
Source publication
Peanut (Arachis hypogaea L.) is an economically important legume crop in irrigated production areas of northern Australia. Although the potential
pod yield of the crop in these areas is about 8 t ha−1, most growers generally obtain around 5 t ha−1, partly due to poor irrigation management. Better information and tools that are easy to use, accurate...
Context in source publication
Context 1
... the area sown to dryland peanuts has declined significantly. To adapt to this impact, the industry has been transforming itself by shifting its production base from the dryland areas in the South Burnett (26.6°S, 152°E) and North Burnett (25.6°S, 151.6°E) districts to irrigated production regions throughout Queensland and the Northern Territory (Fig. 1). The key areas growing irrigated peanuts are now located in the Atherton Tableland (17°S, 145°E) and Georgetown (18.3°S, 143.5 °E) in northern Queensland; Bundaberg (*25°S, 152.5°E) and Childers (25.2°S, 152.3°E) in southern coastal Queensland; Emerald (23.5°S, 148.2°E) and the Mackenzie Valley to the east of the Mackenzie river (23°S ...
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Citations
... Currently, the peanut planting areas further expand into Katherine in the Northern Territory and other Queensland areas, i.e. Texas, Inglewood, St. George, Childers, Chinchilla, and Georgetown (Chauhan et al. 2013). ...
... Like other crops, peanut crops can also be affected by climate change. In Australia, peanuts are traditionally cultivated in dryland conditions (Chauhan et al. 2013). Unfortunately, since Australia's climate is highly variable (Nguyen-Huy et al. 2020) due to the impact of El Nino-Southern Oscillation (ENSO) (Nicholls, Drosdowsky, and Lavery 1997), unfavorable weather conditions, for instance drought and excessive rainfall, can easily affect peanut production in the country (Meinke, Stone, and Hammer 1996). ...
... Current peanut planting areas, which will not be suitable in 2100 include Katherine in the Northern Territory and Georgetown, Emerald, St. George, Chinchilla, Inglewood, and Texas in Queensland. These areas are the expansion of peanut growing regions in Australia, due to decreasing productivity in the traditional dryland peanut regions in the South and North Burnett (Chauhan et al. 2013). ...
One of the major impacts of climate change in the agricultural sector relates to changes in the suitability of areas that are used for planting crops. Since peanut (Arachis hypogaea L.) is one of the most important sources of protein, an assessment of the potential shifts in peanut crop planting areas is critical. In this study, we evaluated the effects of climate change on the potential distribution of peanut crops in Australia. The current and potential future distributions of peanut crops were modeled using Species Distribution Models (SDMs) of CLIMatic indEX (CLIMEX). The future potential peanut crop distributions in Australia for 2030, 2050, 2070, and 2100 were modeled by employing CSIRO-Mk3.0 and MIROC-H Global Climate Models (GCMs) under SRES A2 climate change scenarios from CliMond 10’ database. The results indicated an increase in unsuitable areas for peanut cultivation in Australia throughout the projected years for the two GCMs. The CSIRO-Mk3.0 projection of unsuitable areas in 2100 was higher (i.e. 76% of the Australian continent) than the MIROC-H projection (i.e. 48% of the Australian continent). It was found that the projected increase in dry stress in the future could cause limitations in areas that are currently suitable for peanut crop cultivation. Looking into the future suitability of existing peanut cultivation areas, both GCMs agreed that some areas will become unsuitable, while they disagreed with the suitability of other areas. However, they agreed on the important point that only a small number of existing peanut cultivation areas would still be suitable in the future. Using CLIMEX, the present study has confirmed the effects of climate change on the shifts in areas suitable for peanut cultivation in the future, and thus may provide valuable information relevant to the long-term planning of peanut cultivation in Australia.
... It uses technical methods to reduce requirements of a user's specialized knowledge and can take a user's managerial experience into account. It can also maximize their productivity and improve irrigation water-use efficiency [56,57]. ...
Accurate short-term forecasts of daily reference evapotranspiration (ET0) are essential for real-time irrigation scheduling. Many models rely on current and historical temperature data to estimate daily ET0. However, easily accessible temperature forecasts are relatively less reported in short-term ET0 forecasting. Furthermore, the accuracy of ET0 forecasting from different models varies locally and also across regions. We used five temperature-dependent models to forecast daily ET0 for a 7-day horizon in the North China Plain (NCP): the McCloud (MC), Hargreaves-Samani (HS), Blaney-Criddle (BC), Thornthwaite (TH), and reduced-set Penman–Monteith (RPM) models. Daily meteorological data collected between 1 January 2000 and 31 December 2014 at 17 weather stations in NCP to calibrate and validate the five ET0 models against the ASCE Penman–Monteith (ASCE-PM). Forecast temperatures for up to 7 d ahead for 1 January 2015–19 June 2021 were input to the five calibrated models to forecast ET0. The performance of the five models improved for forecasts at all stations after calibration. The calibrated RPM is the preferred choice for forecasting ET0 in NCP. In descending order of preference, the remaining models were ranked as HS, TH, BC, and MC. Sensitivity analysis showed that a change in maximum temperature influenced the accuracy of ET0 forecasting by the five models, especially RPM, HS, and TH, more than other variables. Meanwhile, the calibrated RPM and HS equations were better than the other models, and thus, these two equations were recommended for short-term ET0 forecasting in NCP.
... Fuzzyinference and knowledge based user-friendly optimization tool is developed for Wheat and corn crops [135]. Chauhan et al. [136] proposed a web-based DSS to enhance irrigation water management for peanut crop on APSIM platform. Gavilán et al. [137] measured the daily greenhouse crop evapotranspiration for strawberry and found more accuracy using empirical methods and sensors based on soil moisture. ...
From last decade, Big data analytics and machine learning is a hotspot research area in the domain of agriculture. Agriculture analytics is a data-intensive multidisciplinary problem. Big data analytics becomes a key technology to perform analysis of voluminous data. Irrigation water management is a challenging task for sustainable agriculture. It depends on various parameters related to climate, soil and weather conditions. For accurate estimation of requirement of water for a crop a strong modeling is required. This paper aims to review the application of big data based decision support system framework for sustainable water irrigation management using intelligent learning approaches. We examined how such developments can be leveraged to design and implement the next generation of data, models, analytics and decision support tools for agriculture irrigation water system. Moreover, water irrigation management need to rapidly adapt state-of-the-art using big data technologies and ICT information technologies with the focus of developing application based on analytical modeling approach. This study introduces the area of research, including a irrigation water management in smart agriculture, the crop water model requirement, and the methods of irrigation scheduling, decision support system, and research motivation.
... plants are not extensively considered (Bonachela et al., 2006;Chauhan et al., 2013). Consequently, such approaches usually lead to over-or under-fertigation, which can adversely influence the crop yield and quality and promote the waste of water and energy resources (Bonachela et al., 2006;Liu and Xu, 2018;Prenger et al., 2005;Shin and Son, 2016). ...
Highlights
An on-the-go monitoring system was developed for assessing spatial and temporal variations in crop growth.
The system was used to realize real-time transpiration estimation for lettuce growth.
A variable-rate fertigation system was developed based on on-the-go transpiration monitoring.
The variable-rate fertigation system was validated in controlled-environment cultivation of hydroponic lettuce.
Abstract . Variable-rate fertigation, as a key component of hydroponic cultivation, ensures the sustainable use of water. However, this fertigation method is challenging to implement because of the difficulty associated with assessing the water needs of plants, which vary with plant growth stage and environmental conditions. In this study, an on-the-go crop growth monitoring system was developed to measure the canopy cover of lettuce plants on a daily basis. The canopy cover during the vegetative growth period could be estimated with an accuracy of 98.5% ±1.7% compared to manual segmentation. A modified Penman-Monteith equation was used to predict the transpiration rate of the lettuce plants. In the first cultivation test, the transpiration rate model exhibited a highly linear relationship with a slope of 0.91, coefficient of determination of >0.9, and standard error of regression of <0.51, compared to the directly measured transpiration rates. In the second cultivation test, an automated system that could variably control the fertigation volume was incorporated with the on-the-go system for monitoring the lettuce transpiration in real time. The proposed system could realize on-the-go mapping of the spatial distribution of the canopy cover of the growing lettuce plants, and the variable fertigation responded to the daily water consumption of the plants with an error of only 7.3%. Moreover, the stable canopy cover growth indicated the higher efficiency of variable fertigation compared to timer-based fertigation systems. The proposed variable-rate fertigation system is a promising and practical approach to implement hydroponics with reduced water and energy consumption. Keywords: Canopy cover, Hydroponic, On-the-go monitoring, Transpiration, Variable fertigation.
... A crop growth model can be used to predict genetic variability and agronomic performance (i.e., yield) of crop cultivars subjected to various environmental conditions. Many previous studies have determined effects of crop management and climatic conditions on yield through simulation (i.e., cultivar [5]; sowing dates [6]; row spacing [7,8]; irrigation [9]; diverse climatic conditions [10]. Battisti et al. [11] have used four crop models (AQUACROP, MONICA, DSSAT, and APSIM) to predict trends of soybean yield as affected by climate change. ...
Soybean sprout is an important food ingredient in East Asian cuisine. Soybean growth is highly sensitive to temperature and photoperiod. Thus, it is important to determine the optimal base temperature for an accurate yield prediction. The optimal base temperature can be varied by cultivars. In this study, six soybean sprout cultivars that are commonly grown in Korea were planted in South Jeolla province, South Korea between 2003 and 2018. Data on phenology were collected from the field and used to determine the optimal base temperature for each cultivar. As a result, variations of optimal base temperatures of cultivars ranged from 0 °C to 15 °C. In simulation, three plant parameter sets, including Soy15, Soy6, and Soy0, were created. Soy15, Soy6, and Soy0 represented soybean cultivars with base temperatures of 15 °C, 6 °C, and 0 °C, respectively. In simulation results, the values of percent bias were under 15%, indicating that the Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) could reasonably simulate soybean yields. Among these three cultivars, Soy15 had the smallest yield, while Soy6 had the highest yield. In climate change scenarios (SSP245 and SSP585), both maximum and minimum temperatures were increased by 1–3.3 °C. With increasing temperatures in the future period, grain yields for all cultivars decreased. The yield reduction might be because the high temperature shortened the length of growth period of the soybeans. Among the three cultivars, Soy6 was a promising cultivar that could have a high yield under climate change scenarios.
... With an index below 20%, crops could be harvested at full maturity, whereas index above 20%, harvest could be advanced up to about 2 weeks to reduce the risk of contamination . The irrigation-scheduling programme 'Aquaman' tailored for peanut is being used by growers to achieve both high yield and low aflatoxin risk (Chauhan et al., 2013). ...
Peanut is presently grown in over 26 Mha worldwide with a total production of over 45 Mt. About 80% of the world peanut production comes from rainfed regions of the semiarid tropics, where the yields are generally low and variable due to erratic water deficits and elevated temperature.
Peanut generally responds favourably to water deficit applied from emergence to start of flowering, resulting in increased pod yields. Sensitivity to water deficit increases progressively during the reproductive phase.
Biomass accumulation in peanut is usually proportional to the amount of water transpired by the plant. Researchers have demonstrated variation in transpiration efficiency (TE) between peanut genotypes with similar transpiration (T). Tis chapter describes genotypic, environmental, and management factors affecting T and TE.
This chapter describes major nutrient deficiencies that affect productivity and seed quality production in many regions of the world.
With increasing drought frequency in semiarid tropics, there is interest in breeding shorter duration cultivars. However, much of the short-duration germplasm available in the global gene banks has low yield in favourable environments, and poor seed quality and foliar disease resistance. Major introgression effort to incorporate all these traits into adapted early-maturing genotypes has been largely successful over the past decade.
In 1950’s peanut has been described as ‘the unpredictable legume’ because of its unpredictable responses to inputs. However, the physiological principles developed in the past decades have been successfully applied in peanut crop models making the crop performance predictable across diverse environments.
... Since A. flavus and AFs are more prevalent in areas with a tropical climate, a large number of model developments concentrate on peanuts. While peanut, as an important crop in the southern USA and as a main staple in Africa, might seem less important in other countries in temperate regions, investigating the experiences of at least some peanut models can be useful for models with maize in focus [107][108][109][110][111][112][113][114]. Furthermore, some peanut models are direct predecessors of maize models, as they were used as a starting point by maize model developers [83,109,114]. ...
Aflatoxins (AFs) are harmful secondary metabolites produced by various moulds, among which Aspergillus flavus is the major AF-producer fungus. These mycotoxins have carcinogenic or acute toxigenic effects on both humans and food producing animals and, therefore, the health risks and also the potential economic damages mounted by them have led to legal restrictions, and several countries have set maximum allowable limits for AF contaminations in food and feed. While colonization of food and feed and AF production by A. flavus are highly supported by the climatic conditions in tropical and subtropical geographic regions, countries in the temperate climate zones are also increasingly exposed to AF-derived health risks due to climate change. In the present study, we have reviewed the available mathematical models as risk assessment tools to predict the possibility of A. flavus infection and levels of AF contaminations in maize in a changing climatic environment. After highlighting the benefits and possible future improvements of these models, we summarize the current agricultural practices used to prevent or, at least, mitigate the deleterious consequences of AF contaminations.
... Generally, peanut crops are grown under dry culture practice on large-scale farms with fully mechanised systems (Pitt and Hocking 2006). The cultivation areas spread across the eastern part of Queensland and the northern part of the Northern Territory (Chauhan et al. 2013;Crosthwaite 1994). Unfortunately, aflatoxin contamination in peanut is the dominant mycotoxin problem in Australia (Pitt and Hocking 2006). ...
Because of its negative effect on health, aflatoxin has become one of the most important mycotoxins in the world. As climate stress is one of the main triggers of aflatoxin incidence, climate change could affect its geographic distribution. The primary aim of this study was to examine the effect of climate change on the future distribution of aflatoxin in peanut (Arachis hypogaea L.) crops in Australia. The projected distributions in 2030, 2050, 2070, and 2100 were modelled by employing the CLIMEX (CLIMatic indEX) model using two global climate models (GCMs), i.e. CSIRO-Mk3.0 and MIROC-H based on SRES A2 and SRES A1B climate scenarios. This study has successfully developed CLIMEX model parameters for aflatoxin and confirmed the climatic zone preference of aflatoxin incidence, as concluded by other studies. Therefore, the model parameters are applicable in all parts of the world. The projection results in Australia confirm that climate change affects the future distribution of aflatoxin, including the distribution in the current peanut-growing areas. Shifts in aflatoxin invasion areas from the tropical and subtropical climate zones of the eastern part of Australia to the temperate climate zones of the south-eastern and south-western parts of the country were projected by 2100. Thus, adaptation and mitigation measures are needed to overcome the negative impacts in the future. Options for these measures include relocation of planting areas, development of host-plant resistance, proper agricultural practices, and mitigation actions by using physical, chemical, and biological approaches.
... Decision support tools such as APSIM (Keating, 2003), CANEGRO (Singels and Bezuidenhout, 2002), or WATERSENSE (Inman-Bamber et al., 2007, 2007 can provide accurate predictions of crop development and soil water balance (Inman-Bamber and McGlinchey, 2003). Therefore, they are turned to as a solution to this problem (Chauhan et al., 2013). However, these decision support tools require frequent user-interactions to obtain precise irrigation scheduling. ...
Better irrigation practices, through increased water use efficiency, can deliver economic, environmental and social benefits to agro-ecological systems. Increasingly, farmers world-wide are turning to automated irrigation systems to save them a significant amount of time by remotely turning on and off pumps and valves. Unfortunately, automated irrigation systems on their own do not provide insight on (i) the amount and timing of irrigation required by the crop or, (ii) how irrigation schedules should change with soil type, farm management and climate. To unravel these complex interactions, an irrigation decision support tool is needed. However, due to the high frequency of irrigations across dozens of irrigation blocks, and the need to irrigate almost all year-round, irrigation decision support tools can be very tedious and time-consuming for farmers to use daily. For these reasons, many farmers will not adopt these tools, and in doing so, fail to optimise irrigation use efficiency due to the multi-factorial nature of the farming system. This paper describes a cybernetic closed-loop solution that was piloted on a sugarcane farm in north-eastern Australia. The solution seeks to improve irrigation management by seamlessly integrating the WiSA automated irrigation system with the IrrigWeb irrigation decision support tool. Specifically, an Uplink program and a Downlink program were implemented in the pilot study. The Uplink program saved the farmer a significant amount of time. Instead of the farmer manually entering in records, Uplink uploaded irrigation and rainfall data directly to the irrigation decision support tool. The Downlink program calculated and applied irrigation schedules automatically using IrrigWeb, but also incorporated practical constraints, such as energy, pumping capability, irrigation priorities and farmer irrigation preference. The simulation results demonstrated that the developed closed-loop solution could effectively manage irrigation scheduling by incorporating irrigation decision support tools with practical constraints. Systems that increase water use efficiency can deliver practical, profitable and environmental benefits to irrigated agricultural systems worldwide.
... Thysen and Detlefsen (2006) described an internet implementation of a previous stand-alone PC-based irrigation decisionmaking program based essentially on an ET and water balance method. Chauhan et al. (2013) introduced a web-based DSS, AQUAMAN, to assist Australian peanut (Arachis hypogaea L.) growers to schedule irrigation based on the ET-WB method introduced in FAO-56. Bartlett et al. (2015) developed a smartphone app to extend the usage of the Water Irrigation Scheduling for Efficient Application system, an ET-WB-based IS tool developed by Colorado State University. ...
In an effort to improve plant growth and to achieve high yield and/or quality, irrigation scheduling (IS) seeks to provide plants with appropriate quantities of water at appropriate times. To better understand irrigation scheduling's main processes and principles, its four most common methods of operation-(1) evapotranspiration and water balance (ET-WB), (2) soil moisture (Θ) status, (3) plant water status, and (4) models-along with their pros and cons are introduced and compared. Irrigation applications, including software, programs, and associated controllers are introduced. Given that some of these methods focus on Θ or plant responses to soil moisture, the determination of target soil moisture levels, along with estimates (either calculated or measured) of current soil moisture status are key to both scheduling irrigations, and the precise replenishment of soil moisture to target levels. Accordingly, factors in the soil-crop-atmosphere system affecting soil moisture must be considered in the scheduling process. As all four types of IS methods focus on soil water content, which serves as a bridge between irrigation management and crop water requirements for growth, future scheduling methods should focus on the management of soil moisture based on an advanced understanding of its effects on crop growth either by the integration of existing IS methods or the development of new models, using intelligent algorithms. Using these approaches, more practical, accurate, and easily adaptable IS applications should be developed for real-time farming operations. Weather station networks and online data access should be enhanced to better serve these IS applications.
