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Article ; Online: Yield gains of irrigated crops in Australia have stalled

Albert Muleke / Matthew Tom Harrison / Maria Yanotti / Martin Battaglia

Current Research in Environmental Sustainability, Vol 4, Iss , Pp 100192- (2022)

the dire need for adaptation to increasingly volatile weather and market conditions

2022

Abstract: The climate crisis demands the development of innovations that sustainably raise farm-gate profit under increasingly volatile conditions. Here, we review the literature on the Australian irrigated grains sector and show that yield gains have not ... ...

Abstract	The climate crisis demands the development of innovations that sustainably raise farm-gate profit under increasingly volatile conditions. Here, we review the literature on the Australian irrigated grains sector and show that yield gains have not progressed since 2002. We reveal a concerning trend of increasing demand for irrigation water on the one hand, yet declining availability of irrigation water on the other. We show that yield gains of Australian irrigated crops have not progressed since 2002, although the use of irrigation water has declined since 2013 and water-use efficiency of irrigated crops has marginally increased. These trends suggest that productivity gains realised by the adoption of new technology, skills and practices over time (including new crop genotypes, larger machinery, reduced tillage, automated irrigation sensors etc) have not been enough to overcome background changes in climatic and economic factors that influence yields of irrigated crops at the continental scale. We highlight a cruel irony that despite having the ability to alleviate water stress, farmers with access to irrigation are still very much dependent on rainfall, because low rainfall reduces regional irrigation supply and elevates water prices, making use of irrigation financially unviable. This, together with hastened crop development and higher risk of heat-induced floret sterility, has meant that the climate emergency has detrimentally impacted on yield gains of irrigated crops, although detrimental impacts have been mediated by rising atmospheric CO2. We conclude that the greatest potential for improving the profitability and water-use efficiency of irrigated crops may be through adoption of integrated combinations of site-specific whole farm packages, including contextualised agronomic, financial and engineering interventions. Appropriate decision support system (DSS) frameworks can help users unpack some of this complexity, enabling land stewards to tactically navigate volatile climatic and market conditions to ...
Keywords	Drought ; Irrigation infrastructure ; Adaptation ; Extreme weather events ; Mitigation ; Environmental sciences ; GE1-350 ; Environmental protection ; TD169-171.8
Subject code	571
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Simulating Soil-Plant-Climate Interactions and Greenhouse Gas Exchange in Boreal Grasslands Using the DNDC Model

Daniel Forster / Jia Deng / Matthew Tom Harrison / Narasinha Shurpali

Land, Vol 11, Iss 1947, p

2022 Volume 1947

Abstract: With global warming, arable land in boreal regions is tending to expand into high latitude regions in the northern hemisphere. This entails certain risks; such that inappropriate management could result in previously stable carbon sinks becoming sources. ...

Abstract	With global warming, arable land in boreal regions is tending to expand into high latitude regions in the northern hemisphere. This entails certain risks; such that inappropriate management could result in previously stable carbon sinks becoming sources. Agroecological models are an important tool for assessing the sustainability of long-term management, yet applications of such models in boreal zones are scarce. We collated eddy-covariance, soil climate and biomass data to evaluate the simulation of GHG emissions from grassland in eastern Finland using the process-based model DNDC. We simulated gross primary production (GPP), net ecosystem exchange (NEE) and ecosystem respiration (Reco) with fair performance. Soil climate, soil temperature and soil moisture at 5 cm were excellent, and soil moisture at 20 cm was good. However, the model overestimated NEE and Reco following crop termination and tillage events. These results indicate that DNDC can satisfactorily simulate GHG fluxes in a boreal grassland setting, but further work is needed, particularly in simulated second biomass cuts, the (>20 cm) soil layers and model response to management transitions between crop types, cultivation, and land use change.
Keywords	ecophysiological modelling ; boreal agriculture ; greenhouse gases ; model evaluation ; DNDC ; soil organic carbon ; Agriculture ; S
Subject code	550
Language	English
Publishing date	2022-11-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Advancing Skyborne Technologies and High-Resolution Satellites for Pasture Monitoring and Improved Management

Michael Gbenga Ogungbuyi / Caroline Mohammed / Iffat Ara / Andrew M. Fischer / Matthew Tom Harrison

Remote Sensing, Vol 15, Iss 4866, p

A Review

2023 Volume 4866

Abstract: The timely and accurate quantification of grassland biomass is a prerequisite for sustainable grazing management. With advances in artificial intelligence, the launch of new satellites, and perceived efficiency gains in the time and cost of the ... ...

Abstract	The timely and accurate quantification of grassland biomass is a prerequisite for sustainable grazing management. With advances in artificial intelligence, the launch of new satellites, and perceived efficiency gains in the time and cost of the quantification of remote methods, there has been growing interest in using satellite imagery and machine learning to quantify pastures at the field scale. Here, we systematically reviewed 214 journal articles published between 1991 to 2021 to determine how vegetation indices derived from satellite imagery impacted the type and quantification of pasture indicators. We reveal that previous studies have been limited by highly spatiotemporal satellite imagery and prognostic analytics. While the number of studies on pasture classification, degradation, productivity, and management has increased exponentially over the last five years, the majority of vegetation parameters have been derived from satellite imagery using simple linear regression approaches, which, as a corollary, often result in site-specific parameterization that become spurious when extrapolated to new sites or production systems. Few studies have successfully invoked machine learning as retrievals to understand the relationship between image patterns and accurately quantify the biophysical variables, although many studies have purported to do so. Satellite imagery has contributed to the ability to quantify pasture indicators but has faced the barrier of monitoring at the paddock/field scale (20 hectares or less) due to (1) low sensor (coarse pixel) resolution, (2) infrequent satellite passes, with visibility in many locations often constrained by cloud cover, and (3) the prohibitive cost of accessing fine-resolution imagery. These issues are perhaps a reflection of historical efforts, which have been directed at the continental or global scales, rather than at the field level. Indeed, we found less than 20 studies that quantified pasture biomass at pixel resolutions of less than 50 hectares. As such, the use of ...
Keywords	AI ; end user ; grassland management ; land-use ; machine learning ; pasture biomass ; Science ; Q
Subject code	710
Language	English
Publishing date	2023-10-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: An Integrated Economic, Environmental and Social Approach to Agricultural Land-Use Planning

Sahar Shahpari / Janelle Allison / Matthew Tom Harrison / Roger Stanley

Land, Vol 10, Iss 364, p

2021 Volume 364

Abstract: Agricultural land-use change is a dynamic process that varies as a function of social, economic and environmental factors spanning from the local to the global scale. The cumulative regional impacts of these factors on land use adoption decisions by ... ...

Abstract	Agricultural land-use change is a dynamic process that varies as a function of social, economic and environmental factors spanning from the local to the global scale. The cumulative regional impacts of these factors on land use adoption decisions by farmers are neither well accounted for nor reflected in agricultural land use planning. We present an innovative spatially explicit agent-based modelling approach (Crop GIS-ABM) that accounts for factors involved in farmer decision making on new irrigation adoption to enable land-use predictions and exploration. The model was designed using a participatory approach, capturing stakeholder insights in a conceptual model of farmer decisions. We demonstrate a case study of the factors influencing the uptake of new irrigation infrastructure and land use in Tasmania, Australia. The model demonstrates how irrigated land-use expansion promotes the diffusion of alternative crops in the region, as well as how coupled social, biophysical and environmental conditions play an important role in crop selection. Our study shows that agricultural land use reflected the evolution of multiple simultaneous interacting biophysical and socio-economic drivers, including soil and climate type, crop and commodity prices, and the accumulated effects of interactive decisions of farmers.
Keywords	land use changes ; spatial agent-based modelling ; stakeholder insights ; irrigation expansion ; Agriculture ; S
Subject code	910
Language	English
Publishing date	2021-04-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Selenium Nanoparticles Improve Physiological and Phytochemical Properties of Basil ( Ocimum basilicum L.) under Drought Stress Conditions

Javad Asghari / Hassan Mahdavikia / Esmaeil Rezaei-Chiyaneh / Farzad Banaei-Asl / Mostafa Amani Machiani / Matthew Tom Harrison

Land, Vol 12, Iss 164, p

2023 Volume 164

Abstract: Drought impacts on food security, land degradation and rates of biodiversity loss. Here, we aimed to investigate selenium nanoparticles (Se NPs) influenced plant resilience to drought using the morphological, physiological, and essential oil (EO) ... ...

Abstract	Drought impacts on food security, land degradation and rates of biodiversity loss. Here, we aimed to investigate selenium nanoparticles (Se NPs) influenced plant resilience to drought using the morphological, physiological, and essential oil (EO) quantity and quality of basil ( Ocimum basilicum L.) as drought proxies. Treatments included irrigation at 100% field capacity (FC100) as no stress, 80% FC as moderate water stress (FC80) and 60% FC as severe water stress (FC60), together with application of Se NPs at either 0 mg L −1 (control), 50 mg L −1 , or 100 mg L −1 . The highest (257 g m −2 ) and lowest (185 g m −2 ) dry matter yields were achieved in nil-stress and severe-water-stress conditions, respectively. Dry matter yields decreased by 15% and 28% under moderate and severe water stress, respectively. Applying Se NPs enhanced the dry matter yields by 14% and 13% for the 50 and 100 mg L −1 treatments, respectively. The greatest EO content (1.0%) and EO yield (1.9 g m −2 ) were observed under severe water stress. Applying Se NPs of 50 and 100 mg L −1 enhanced the essential oil content by 33% and 36% and the essential oil yield by 52% and 53%, respectively. We identified 21 constituents in the EO, with primary constituents being methyl chavicol (40%–44%), linalool (38–42%), and 1,8-cineole (5–6%). The greatest methyl chavicol and linalool concentrations were obtained in FC80 with 50 mg L −1 Se NPs. The highest proline (17 µg g −1 fresh weight) and soluble sugar content (6 mg g −1 fresh weight) were obtained under severe water stress (FC60) for the 50 mg L −1 Se NP treatment. Our results demonstrate that low-concentration Se NPs increase plant tolerance and improve the EO quantity and quality of basil under drought stress.
Keywords	antioxidant activity ; crop production ; essential oil ; soluble sugar ; water restriction ; water deficit ; Agriculture ; S
Subject code	333
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Estimating Pasture Biomass Using Sentinel-2 Imagery and Machine Learning

Yun Chen / Juan Guerschman / Yuri Shendryk / Dave Henry / Matthew Tom Harrison

Remote Sensing, Vol 13, Iss 4, p

2021 Volume 603

Abstract: Effective dairy farm management requires the regular estimation and prediction of pasture biomass. This study explored the suitability of high spatio-temporal resolution Sentinel-2 imagery and the applicability of advanced machine learning techniques for ...

Abstract	Effective dairy farm management requires the regular estimation and prediction of pasture biomass. This study explored the suitability of high spatio-temporal resolution Sentinel-2 imagery and the applicability of advanced machine learning techniques for estimating aboveground biomass at the paddock level in five dairy farms across northern Tasmania, Australia. A sequential neural network model was developed by integrating Sentinel-2 time-series data, weekly field biomass observations and daily climate variables from 2017 to 2018. Linear least-squares regression was employed for evaluating the results for model calibration and validation. Optimal model performance was realised with an R 2 of ≈0.6, a root-mean-square error (RMSE) of ≈356 kg dry matter (DM)/ha and a mean absolute error (MAE) of 262 kg DM/ha. These performance markers indicated the results were within the variability of the pasture biomass measured in the field, and therefore represent a relatively high prediction accuracy. Sensitivity analysis further revealed what impact each farm’s in situ measurement, pasture management and grazing practices have on the model’s predictions. The study demonstrated the potential benefits and feasibility of improving biomass estimation in a cheap and rapid manner over traditional field measurement and commonly used remote-sensing methods. The proposed approach will help farmers and policymakers to estimate the amount of pasture present for optimising grazing management and improving decision-making regarding dairy farming.
Keywords	remote sensing ; deep learning ; digital agriculture ; dairy farming ; grazing ; grassland biomass ; Science ; Q
Language	English
Publishing date	2021-02-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Physiological and Molecular Responses of Wheat to Low Light Intensity

Xiu Li / Rui Yang / Liulong Li / Ke Liu / Matthew Tom Harrison / Shah Fahad / Mingmei Wei / Lijun Yin / Meixue Zhou / Xiaoyan Wang

Agronomy, Vol 13, Iss 272, p

2023 Volume 272

Abstract: Here we document physiological and molecular attributes of three wheat cultivars (ZM9023, YM158 and FM1228) under low light intensity with advanced technologies, including non-standard quantitative technology and quantitative proteomics technology. We ... ...

Abstract	Here we document physiological and molecular attributes of three wheat cultivars (ZM9023, YM158 and FM1228) under low light intensity with advanced technologies, including non-standard quantitative technology and quantitative proteomics technology. We found lower dry matter accumulation of YM158 compared with ZM 9023 and FM1228 under low light intensities due to up-regulation of photosynthetic parameters electron transport rate (ETR), Y(II), Fv/Fm, Chl (a + b) of YM158 and down-regulation of Chl a/b. ETR, Y(II) and Fv/Fm significantly decreased between ZM9023 and FM1228. The ETR between PSII and PSI of YM158 increased, while light use efficiency (LUE) of ZM9023 and FM1228 decreased. We found that YM158 had greater propensity to adapt to low light compared with ZM9023, as the former was able to increase photochemical electron transfer rate, enhance photosystem activity, and increase the light energy under low light. This meant that the YM158 flag leaf has stronger regulatory mechanism under low light environment. Through proteomic analysis, we found LHC protein (LHCB1, LHCB4, LHCA2, LHCA3) for YH158 was significantly up-regulated, while the PSII subunit protein of FM1228 and ZM9023 b559 subunit protein were down-regulated. We also documented enhanced light use efficiency (LUE) due to higher light capture pigment protein complex (LHC), photosystem II (PSII), PSI and cytochrome B6F-related proteins, with dry matter accumulation being positively correlated with Fv/Fm, ETR, and ΦPS(II), and negatively correlated with initial fluorescence F0. We suggest that Fv/Fm, ETR, and ΦPS(II) could be considered in shade tolerance screening to facilitate wheat breeding.
Keywords	low light intensity ; photon ; wheat ; chlorophyll ; fluorescence ; proteomics ; Agriculture ; S
Subject code	580
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Enabling Regenerative Agriculture Using Remote Sensing and Machine Learning

Michael Gbenga Ogungbuyi / Juan P. Guerschman / Andrew M. Fischer / Richard Azu Crabbe / Caroline Mohammed / Peter Scarth / Phil Tickle / Jason Whitehead / Matthew Tom Harrison

Land, Vol 12, Iss 1142, p

2023 Volume 1142

Abstract: The emergence of cloud computing, big data analytics, and machine learning has catalysed the use of remote sensing technologies to enable more timely management of sustainability indicators, given the uncertainty of future climate conditions. Here, we ... ...

Abstract	The emergence of cloud computing, big data analytics, and machine learning has catalysed the use of remote sensing technologies to enable more timely management of sustainability indicators, given the uncertainty of future climate conditions. Here, we examine the potential of “regenerative agriculture”, as an adaptive grazing management strategy to minimise bare ground exposure while improving pasture productivity. High-intensity sheep grazing treatments were conducted in small fields (less than 1 ha) for short durations (typically less than 1 day). Paddocks were subsequently spelled to allow pasture biomass recovery (treatments comprising 3, 6, 9, 12, and 15 months), with each compared with controls characterised by lighter stocking rates for longer periods (2000 DSE/ha). Pastures were composed of wallaby grass ( Austrodanthonia species ), kangaroo grass ( Themeda triandra ), Phalaris ( Phalaris aquatica ), and cocksfoot ( Dactylis glomerata ), and were destructively sampled to estimate total standing dry matter (TSDM), standing green biomass, standing dry biomass and trampled biomass. We invoked a machine learning model forced with Sentinel-2 imagery to quantify TSDM, standing green and dry biomass. Faced with La Nina conditions, regenerative grazing did not significantly impact pasture productivity, with all treatments showing similar TSDM, green biomass and recovery. However, regenerative treatments significantly impacted litterfall and trampled material, with high-intensity grazing treatments trampling more biomass, increasing litter, enhancing surface organic matter and decomposition rates thereof. Pasture digestibility and sward uniformity were greatest for treatments with minimal spelling (3 months), whereas both standing senescent and trampled material were greater for the 15-month spelling treatment. TSDM prognostics from machine learning were lower than measured TSDM, although predictions from the machine learning approach closely matched observed spatiotemporal variability within and across ...
Keywords	machine learning ; satellite imagery ; regenerative grazing ; grassland biomass ; total standing dry matter ; digital agriculture ; Agriculture ; S
Subject code	550
Language	English
Publishing date	2023-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Biochemical Response of Okra ( Abelmoschus esculentus L.) to Selenium (Se) under Drought Stress

Jawad Ali / Ibadullah Jan / Hidayat Ullah / Shah Fahad / Shah Saud / Muhammad Adnan / Baber Ali / Ke Liu / Matthew Tom Harrison / Shah Hassan / Sunjeet Kumar / Muhammad Amjad Khan / Muhammad Kamran / Mona S. Alwahibi / Mohamed S. Elshikh

Sustainability, Vol 15, Iss 5694, p

2023 Volume 5694

Abstract: Drought stress restricts the growth of okra ( Abelmoschus esculentus L.) by disrupting its biochemical and physiological functions. The current study was conducted to evaluate the role of selenium (0, 1, 2, and 3 mg Se L −1 as a foliar application) in ... ...

Abstract	Drought stress restricts the growth of okra ( Abelmoschus esculentus L.) by disrupting its biochemical and physiological functions. The current study was conducted to evaluate the role of selenium (0, 1, 2, and 3 mg Se L −1 as a foliar application) in improving okra tolerance to drought (control (100% field capacity-FC), mild stress (70% FC), and severe stress (35% FC)) imposed 30 days after sowing (DAS). Drought (severe) markedly decreased chlorophyll (32.21%) and carotenoid (39.6%) contents but increased anthocyanin (40%), proline (46.8%), peroxidase (POD by 12.5%), ascorbate peroxidase (APX by 11.9%), and catalase (CAT by 14%) activities. Overall, Se application significantly alleviated drought stress-related biochemical disturbances in okra. Mainly, 3 mg Se L −1 significantly increased chlorophyll (21%) as well as anthocyanin (15.14%), proline (18.16%), and antioxidant activities both under drought and control conditions. Selenium played a beneficial role in reducing damage caused by oxidative stress, enhancing chlorophyll and antioxidants contents, and improved plant tolerance to drought stress. Therefore, crops including okra especially, must be supplemented with 3 mg L −1 foliar Se for obtaining optimum yield in arid and semiarid drought-affected areas.
Keywords	abiotic stress ; antioxidant activity ; micronutrient ; photosynthetic pigments ; phenols ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Paradigm Shift towards Beneficial Microbes Enhancing the Efficiency of Organic and Inorganic Nitrogen Sources for a Sustainable Environment

Haji Muhammad / Shah Fahad / Shah Saud / Shah Hassan / Wajid Nasim / Baber Ali / Hafiz Mohkum Hammad / Hafiz Faiq Bakhat / Muhammad Mubeen / Amir Zaman Khan / Ke Liu / Matthew Tom Harrison / Hamada AbdElgawad / Mostafa A. Abdel-Maksoud

Land, Vol 12, Iss 680, p

2023 Volume 680

Abstract: The use of beneficial microbes as biofertilizer has become fundamental in the agricultural sector for their potential role in food safety and sustainable crop production. A field trial was conducted to study the influence of beneficial microbes on the ... ...

Abstract	The use of beneficial microbes as biofertilizer has become fundamental in the agricultural sector for their potential role in food safety and sustainable crop production. A field trial was conducted to study the influence of beneficial microbes on the efficiency of organic and inorganic sources. The experiment was conducted in two consecutive years (2008–2009 and 2009–2010) in a farmer’s field at Dargai Malakand Division. A randomized complete block design was used with four replications. The results revealed a significantly higher straw and grain nitrogen concentrations for the treatments receiving 50% N from urea + 50% N from FYM + BM, followed by the treatments receiving 50% N from urea + 50% N from (FYM + PM) + BM and 120 kg N ha −1 from urea fertilizer, respectively. Comparing the relevant treatments with and without BM, an increasing trend in N concentrations in straw and grain was observed with BM. The results revealed the highest grain total nitrogen, straw total nitrogen and total nitrogen uptake by wheat crop for the treatments receiving 120 kg N ha −1 from urea, followed by the treatments receiving 50% N from urea + 50% N from PM + BM and 50% N from urea + 50% N from (FYM + PM) + BM. Moreover, after comparing the relevant treatments with and without BM, for the parameters mentioned, an increasing trend in nitrogen uptake was observed. Significantly higher total soil nitrogen was obtained for treatment with 50% N from urea + 50% N from FYM + BM, followed by the treatment with 50% N from urea + 50% N from (FYM + PM) + BM or 50% N from urea + 50% N from PM + BM, respectively, as compared to the control treatment plot. Markedly higher soil mineral nitrogen was obtained for the 50% N from urea + 50% N from (FYM + PM) + BM treatment, followed by the treatment with 50% N from urea + 50% N from FYM + BM and 50% N treatment from urea + 50% N from PM + BM, compared to the control treatment. Comparing the relevant treatments with and without BM, an increasing trend in total soil N (g kg −1 soil) and soil mineral ...
Keywords	beneficial microbes ; farmyard manure (FYM) ; poultry manure (PM) ; beneficial microorganisms (BM) ; nitrogen uptake and soil mineral nitrogen ; Agriculture ; S
Subject code	630
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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