Three studies were undertaken during the 2018/2019 growing season (planted in 2018; defoliated, harvested and ginned in 2019) at the Australian Cotton Research Institute (ACRI) in Narrabri (149o36’E,30o12’S) in the Namoi Valley of New South Wales (NSW), on grey cracking clay soil, one at the Irrigation Research and Extension Committee (IREC) in Griffith (34°17′24’S 146°2′24E) in the Murrumbidgee Valley (Southern region) of NSW, on Mundiwa clay loam soil and one at Toobeah (28.4169°S 149.8702°E) in the MacIntyre Valley (Central region) of Queensland (Qld) on grey vertosol soils. Two further studies were undertaken during the 2019/2020 growing season (planted in 2019; defoliated, harvested, and ginned in 2020); one at ACRI and one at Cecil Plains (27.5316°S 151.1930°E) on black vertosol soils on the Darling Downs in Central Qld.
A summary of the respective field operations employed on each of the fields are presented in Table 1. The cotton varieties used for the trials were two CSIRO varieties, containing Bollgard® 3 technology stacked with Roundup Ready Flex, Sicot 746 B3F (Stiller 2016a) and Sicot 714 B3F (Stiller 2016b), currently the two most popular Upland varieties grown in Australia as well as Sicala V-2 (Reid 1995), a popular CSIRO conventional variety grown in the late 1990s. All fields were subjected to standard management practices for irrigated Upland cotton in Australia.
Field A was first subjected to harvest aids by ground rig, with a mixture of leaf defoliant (0.1 L. ha− 1 Dropp® UltraMAX liquid by Bayer Crop Science) and boll opener (1.0 L.ha− 1 Prep® 720 by Bayer Crop Science). The field was again sprayed by ground rig with a mixture of leaf defoliant (0.15 L.ha− 1 Dropp® UltraMAX) and boll opener (1.5 L.ha− 1 Prep® 720). The field was then subjected to a further spray by ground rig with a mixture of leaf defoliant (0.2 L.ha− 1 Dropp® UltraMAX) and boll opener (2.0 L.ha− 1 Prep® 720). The trial was conducted using a randomized complete block design, with four replications. Seed cotton from 32 plots, containing four rows spaced at 1 meter, was harvested by a single row Case IH 1822 spindle harvester (CNH America, Racine, WI), which was maintained and operated via normal industry practice and manufactures recommendations. An average of 0.234 kg seed cotton sample was collected from each replicate produced and was ginned using a 20-saw gin (Continental Eagle, Prattville, AL) with a Mitchell feeder and pre-cleaner located at ACRI.
Field B was first subjected to harvest aids by air, with a mixture of leaf defoliant (0.15 L.ha− 1 Dropp® liquid by Bayer Crop Science), boll opener (0.5 L.ha− 1 Promote® 900 by ADAMA) and crop oil (0.5 L.ha− 1 Canopy® by Caltex). The field was again sprayed by air with a mixture of leaf defoliant (0.15 L.ha− 1 Dropp®), boll opener (2.0 L.ha− 1 Promote® 900) and crop oil (0.5 L.ha− 1 Canopy®). The field was then subjected to a further spray by air with a mixture of leaf defoliant (0.18 L.ha− 1 Dropp®), boll opener (0.9 L.ha− 1 Promote® 900) and crop oil (0.5 L.ha− 1 Canopy®).
The field was harvested using a grower owned and operated John Deere 7760 spindle round module harvester (Moline, IL), with Pro16 row units, which was maintained and operated via normal industry practice and manufacturers recommendations. Only part of the field was utilized for this trial (3.05 ha per treatment), using a randomized complete block design, with three replications. A total of sixty-four part round modules were harvested. The round modules were dropped in the field and picked up by a mast-type tractor mounted implement that holds the module with the axis parallel to the tractor rear axle and were then staged together in the sequence that they were harvested. All modules were ginned, under similar standard commercial conditions, at the Namoi Cotton Limited MacIntyre No. 1 gin, situated in Goondiwindi, Qld. This gin is a Continental Eagle (Prattville, AL) high-capacity saw gin, equipped with four 181 gin stands, with no flow-through air lint cleaners and two stages of controlled-batt saw lint cleaners, capable of producing 60 bales. hour− 1.
Field C was first subjected to harvest aids by ground rig, with a mixture of leaf defoliant (0.175 L.ha− 1 Escalate® UltraMAX liquid by ADAMA) and boll opener (0.5 L.ha− 1 Promote® 900). The field was again sprayed by ground rig with a mixture of leaf defoliant (0.2 L.ha− 1 Escalate® UltraMAX) and boll opener (2.5 L.ha− 1 Promote® 900). The field was then subjected to a further spray by ground rig with a mixture of leaf defoliant (0.15 L.ha− 1 Escalate® UltraMAX ) and boll opener (0.5 L.ha− 1 Promote® 900).
The field was also harvested using a grower owned and operated John Deere 7760 spindle round module harvester (Moline, IL), with Pro16 row units, which was maintained and operated via normal industry practice and manufacturers recommendations. The field utilized for this trial was sown with the two varieties using a randomized complete block design, with four replications, with each replication 0.156 ha. A total of sixteen part round modules were harvested. The round modules were dropped in the field and picked up by a mast-type tractor mounted implement that holds the module with the axis parallel to the tractor rear axle and were then staged together in the sequence that they were harvested. All modules were ginned, under similar standard commercial conditions, at Southern Cotton situated in Leeton, NSW. This gin is a Lummus Corporation (Savannah, GA) high-capacity saw gin, equipped with four 222 gin stands, with one stage of flow-through air lint cleaners and two stages of batt-less saw lint cleaners, capable of producing 60 bales.hour− 1.
Field D was first subjected to harvest aids by ground rig, with a mixture of leaf defoliant (0.1 L.ha− 1 Dropp®), boll opener (1.5 L.ha− 1 Prep®) and adjuvant (0.05 L.ha− 1 Hasten by Victorian Chemicals). The field was sprayed a second time with a mixture of leaf defoliant (0.12 L.ha− 1 Dropp® liquid), boll opener (1.5 L.ha− 1 Prep®) and adjuvant (0.05 L ha− 1 Hasten). The field was sprayed again with leaf defoliant (0.15 L.ha− 1 Dropp®), boll opener (2.0 L ha− 1 Prep®) and adjuvant (0.05 L.ha− 1 Hasten). The trial was conducted using a randomized complete block design, with four replications. Seed cotton from 32 plots, containing four rows spaced at 1 meter, was harvested by a single row Case IH 1822 spindle harvester (Racine, WI), which was maintained and operated via normal industry practice and manufactures recommendations. An average of 0.234 kg seed cotton sample was collected from each replicate produced and was ginned using the same 20-saw gin (Continental Eagle, Prattville, AL) with a Mitchell feeder and pre-cleaner located at ACRI.
Field E was first subjected to harvest aids by air, with a mixture of leaf defoliant (0.2 L.ha− 1 Dropp®), boll opener (0.8 L.ha− 1 Promote® 900) and crop oil (0.5 L.ha− 1 Canopy®). The field was sprayed a second time with a mixture of leaf defoliant (0.2 L ha− 1Dropp® UltraMAX), boll opener (2.2 L.ha− 1 Promote® 900) and crop oil (0.5 L.ha− 1 Canopy®). The trial was conducted using a randomized complete block design, with four replications. Seed cotton from 16 plots, containing 18 rows spaced at 1 meter, was hand harvested, due to the uncertainty created by COVID and accessibility to a mechanical harvester. An average of 0.234 kg seed cotton sample was collected from each replicate produced and was also ginned using the 20-saw gin (Continental Eagle, Prattville, AL) with a Mitchell feeder and pre-cleaner located at ACRI.
For Fields A & D, samples collected after ginning were subjected to objective measurement, as per ASTM D5867 (ASTM 2012a), using an Uster® Technologies AG HVI™ 1000 (Knoxville, TN) at ACRI. Two sub samples of each sample were evaluated for fibre length in terms of upper half mean length (UHML in mm), length uniformity (UI%), short fibre index (fibres < 12.7 mm) (SFI%), elongation (EL%), bundle strength in g.tex− 1 (STR) and micronaire (MIC).
For Fields B & C, classing samples, from opposite sides, of each bale were collected at the gin after bale formation, with samples for Field E collected after ginning. All these samples were subjected to objective measurement, as per ASTM D5867 (ASTM 2012a), using an Uster® Technologies AG HVI 1000 (Knoxville, TN) either at Australian Classing Services in Wee Waa (Fields B & E) and ProClass in Griffith (Field C) respectively, both of which are certified classing facilities as they comply with the requirements of the Australian Cotton Industry’s Best Management Practice Manual for Classing. Two sub samples of each sample were evaluated for UHML, UI%, SFI%, EL%, STR, MIC as well as for colour in terms of yellowness (+ b), reflectance (Rd) and trash in terms of leaf count, % area and leaf grade. Visual classing of the lint was assessed for colour (CG) and visible trash (LG) according to the 2018 grades as established by USDA-AMS, as per ASTM D1684 (ASTM 2012b).
All fibre samples were conditioned under standard conditions of 21+/-1°C and relative humidity % of 65+/-2 as per ASTM D1776 (ASTM 2015).
For all fields, the percentage of the weight of usable fibre per the weight of un-ginned seed cotton (lint turn out) was calculated either by the commercial ginning operators or by technicians at ACRI.
Cotton nitrogen use-efficiency (NUE) was also calculated for evaluating efficiency of the conversion of N fertiliser into cotton lint as per Eq. 1.
![](data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/4RCiRXhpZgAATU0AKgAAAAgABAE7AAIAAAAQAAAISodpAAQAAAABAAAIWpydAAEAAAAgAAAQeuocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAFNhY2hpbiBNYWhhcm51cgAAAeocAAcAAAgMAAAIbAAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAUwBhAGMAaABpAG4AIABNAGEAaABhAHIAbgB1AHIAAAD/4QsiaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wLwA8P3hwYWNrZXQgYmVnaW49J++7vycgaWQ9J1c1TTBNcENlaGlIenJlU3pOVGN6a2M5ZCc/Pg0KPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyI+PHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj48cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0idXVpZDpmYWY1YmRkNS1iYTNkLTExZGEtYWQzMS1kMzNkNzUxODJmMWIiIHhtbG5zOmRjPSJodHRwOi8vcHVybC5vcmcvZGMvZWxlbWVudHMvMS4xLyIvPjxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSJ1dWlkOmZhZjViZGQ1LWJhM2QtMTFkYS1hZDMxLWQzM2Q3NTE4MmYxYiIgeG1sbnM6eG1wPSJodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvIj48eG1wOkNyZWF0ZURhdGU+MjAyMi0wNS0wNlQxMTo1MDozMS45OTc8L3htcDpDcmVhdGVEYXRlPjwvcmRmOkRlc2NyaXB0aW9uPjxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSJ1dWlkOmZhZjViZGQ1LWJhM2QtMTFkYS1hZDMxLWQzM2Q3NTE4MmYxYiIgeG1sbnM6ZGM9Imh0dHA6Ly9wdXJsLm9yZy9kYy9lbGVtZW50cy8xLjEvIj48ZGM6Y3JlYXRvcj48cmRmOlNlcSB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPjxyZGY6bGk+U2FjaGluIE1haGFybnVyPC9yZGY6bGk+PC9yZGY6U2VxPg0KCQkJPC9kYzpjcmVhdG9yPjwvcmRmOkRlc2NyaXB0aW9uPjwvcmRmOlJERj48L3g6eG1wbWV0YT4NCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgPD94cGFja2V0IGVuZD0ndyc/Pv/bAEMABwUFBgUEBwYFBggHBwgKEQsKCQkKFQ8QDBEYFRoZGBUYFxseJyEbHSUdFxgiLiIlKCkrLCsaIC8zLyoyJyorKv/bAEMBBwgICgkKFAsLFCocGBwqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKv/AABEIADkBzwMBIgACEQEDEQH/xAAfAAABBQEBAQEBAQAAAAAAAAAAAQIDBAUGBwgJCgv/xAC1EAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEUMoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+fr/xAAfAQADAQEBAQEBAQEBAAAAAAAAAQIDBAUGBwgJCgv/xAC1EQACAQIEBAMEBwUEBAABAncAAQIDEQQFITEGEkFRB2FxEyIygQgUQpGhscEJIzNS8BVictEKFiQ04SXxFxgZGiYnKCkqNTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqCg4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2dri4+Tl5ufo6ery8/T19vf4+fr/2gAMAwEAAhEDEQA/APpGivLPip4u8VeG/Fuhr4XdZbaO0ub+/sDEjG7ihaPcqsQWVgjMRg9R36VtX/i+a68beAk0O+V9H16K8mmCop85UgV4+SMrgnsR6GgDuaK5DwBrmo63P4rXU7jzxp/iG4srb5FXy4VSMqvAGcFjycnnrXN3/jLXofCnxMvIr7bcaHdyR6e/kxnyFEMbAY24blifmz1oA9TornfD+k6mkdnf3virVtQV4Q721xDaLGxZf+mcCtwTkYbtzmtPVtYttFt0mvIr2RXbYBZ2M10wOM8rEjEDjqRigC/RWPpfiew1i8NtaW+qxyBS+bvSLq2TA/25Y1XPPTOa2KACiiuR07XNRn+Lut6LLcbtPtdMtZ4Ydija7vIGO7GTkKOCccUAddRXK/bLP/hcH2L7bq/23+wvN+x+aPsHl/aMeZs6+dnjd028VlWHxg0fUPDlr4ii0jWo9CnkWKTUpoI0it3L7MODJvIDEAuisoJxuyGAAO/orzzVPGWu2Xxlt9DttH1K7046TJObeA2g85xKgEys8qkKASpBIOT90jmux8Ra7beGfDd/rV+ksltYwtNKkIBcqOuASBn6kUAaVFcja/EXTrm50vzdN1OzsNYkEWn6lcwokFxIwyqgbzIpbB2l0UNjgnIzJYePrPVfE+oaFpel6ld3WmXQt710SJY7cEAiQs0gypyeFy/yN8vTIB1VFchcfEayjS+ubHR9W1LTNOkaK71K0ijMMTIcSYDSLJIE5yY0YcHGSCKt6j44020OkxadBdazdazEZ7K209ULSwhQxlLOyoqYZeWYZLADJoA6SivK/Dvjzy9d+JGual/an9m6SLSUWE4IkttttmWNUZtoO8NnadrHkEggnttT8X2GlWmhXFxDcsmuXkNnbCNVJR5UZ1L5YYGFOcZPsaAN6iuYu/HEEeqX9lpejarrR0zaL6XT0iKW7Fd2z55FaRwuCVjDEZA6nFS6h4xgt9XbStL0rUdav4oFubiCxSNTbRvnZ5hmdFDNtbCZLYBOMc0AdFRXKv8AEXQ/+EXsNatftV2uozi1tLOGH/SJZ8kGLYxAVlKtncQBtJJxWBo3ii81j47zWEkWp6dDb+Hd02mXpwqS/aRiQBGaNiUI+dS3HGQQQAD0miuE+KUhvrXw/wCGRzHr2rw29yM43W8eZpV/ERhfox+hr3fiRND+MGstq+pva6JY+Gobl45JW8mNvPcFgnTcQAOBk8DngUAeh0Vyh8fW66bo8x0TVxqGtB2s9JaKJbplUZZmBkCIApUnc4xuA68VY8JeMYvF/wDaPkaRqWnf2dctaTC/EKnzl5ZQEkY8AqcnAIYYJoA6OivO9f8AEV3pPxu0yzjGo3kE+hTMmnWhyJZhMmGILBFIUN87lQOmeQD0WleNLLV9Gv76Gy1COfTrl7S709rffcxTKQNu1CynOVYMGK7WySADgA6Kq97qFnptuJ9Ru4LSEusYknlCKWY4Vck4yTwB3rB0/wAZi/1y40KXR77S9ZSz+2QWupNEFuI923IkheRQA2Af4huBwa5z4lajaweBBL4wXRdM1U3EYs1F6JTtE8RcxvIkbfd+8AvAxk0Aek0V574v1C2tPFXgfxXpdxFcW93ff2TJNbyBknhuVOz5hwVEkaEe9ehUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRXB+MH/tf4meDvDrjdaxvPq90h6N5KhYR7jzJA3PdB+FPx74i8YaT4k0NdPFrp2iS67Y2Ukx2zT36ysN6gEYiQZIz98kAggdQD0iiuE+Kkh0jT9E8URZWTRdWgaRgcFoJm8mVPoRID9VFd3QBxOtWF5L8aPC99FaTvaQadepLcLGTHGzGPaGboCcHAPXFcXpvhPWvDnxt0DS7XT7iXwvYy317Y3aRkxWiXEOGtyeihZFJUHkiT2r2qigDzrw/NN4F8R+KLXWdO1KS01XVX1SzvLGwmu0cSIisjCFWZGUp/EACDwTWPd+HtYm+FHxAvZNMuo7zxHLPd2uneXvuFjMapGrKufnIXJUZxnHUGvXaKAOF8Fp4Qsrizj0XwvLpepyW4ikuB4ZuLPOFDMGmaFVAJXu3JA6nFdXq2n3Oo26R2esXuksrbjLZpCzOMfdPmxuMd+ADx1q/RQBj6Xot/p94ZrvxNquqR7SvkXcVqqA/3sxQI2fxxz0rYoooAK85a8Og/GnW9R1DT9VayutKtIobi00u5ukZ1eQsuYo2wQCOvrXo1c/c+CtLurqW4lutcV5nLsItfvo0BJydqLMFUegAAHQCgDBs47i++OMWtQWV6unS+F/KW4ntJIQJPtW7YQ6gq2Odpwcc4rj/AOwdY/4ZC/sb+yr7+1Pse37D9mfz8/ac48vG7OOenSvSv+ED0j/n88Qf+FHqH/x+j/hA9I/5/PEH/hR6h/8AH6AMXXkn0r4yaTr89lezaa2jz2LTWdpLcmOUzI4DLGrMoIB+YjHHWrvxb/5JB4n/AOwdL/Krv/CB6R/z+eIP/Cj1D/4/VLV/hhoes6Pdadc3viBYrmMxuf7fvJMA/wCzJKyH6MpHtQBzt9c3vjPSfCOi2+h6laXVvfWd7qD3Vs6Q2scGHbbOR5cpY7QoQk4YkgYIrd8A2F5Z+IfG0l5aTwR3OtmWBpYyolTyYxuUn7wyCMj0q/D8PtHggjiW88QbY1Cj/ior8dBjoJgB+Ap//CB6R/z+eIP/AAo9Q/8Aj9AHIeGry+8HeBbvwpe6Bqt1q1vPcxWv2awkkgvhLKzRyeeAY4wfMG7zGUrgk1DpXh2/+H+u+D7++tbrULO18Pto15JYW0lybabcsofy4wXKsUZdwU44zjNdr/wgekf8/niD/wAKPUP/AI/R/wAIHpH/AD+eIP8Awo9Q/wDj9AHBxaJrfiZfioy6Pdae+vW0C6cl4nl+cBamNTk8AnAJB5XdhsEEUarf6h4itfh7aWHhzW0GnavZT6jLc2EkItSsTqVIdQWGScuuUGBk/MM95/wgekf8/niD/wAKPUP/AI/R/wAIHpH/AD+eIP8Awo9Q/wDj9AHO+Hr648E6x4psNY0nVbj7Zqsup2NxYafNcx3UcwGE3opVHVlKkSFR905281n3uix6b8Tte1PxDZ+I/sGtRW01pcaNNekRvHEI3hlS0bO7gMGI2nLAHIrsv+ED0j/n88Qf+FHqH/x+j/hA9I/5/PEH/hR6h/8AH6AOQ1Hw/FpVp4Q13w5oWqJp+l6pNdXVjKJJ7vy51dGnKMzSM2WDlfvgMcqGBUXNGlvtY+OUmuLo+o2mk/8ACPG0guru2eHznFyGPyMAydTgOAxClgNpBPR/8IHpH/P54g/8KPUP/j9H/CB6R/z+eIP/AAo9Q/8Aj9AGL8Q/9E8Y+ANUk4ii1prVj2DTwOi/+PYH1Nc5438Dap4m+LF3qGnRSQ3Fho9rcabcTxM1pJdRXEjCKT+Fsg/Vchh79drPwt0HW9MezubzX1BZXST+3ryRonU5V1EkjLuBGQSpxV4eAtICgG98QH3PiPUOf/I1AHn+veINe1vxV4H1HSNGu7HXI7K/a5sZbRZJIj+5R1ZJJoQYiclZA/JC46kHd8Iaxa+HPD/iV5LDWr7XLe+N9q9hHaIbl5pgu0xRpI6FCijAWRsBTk5zV3V/hJo+pXUV5aa14n0q+jQxfbbPXbhpmiJyYy0rPhcgHAxyBUukfCnQtKWWRtR8RXl9cY+0382u3ST3O3ITeYnQNtB2jjgUARyWl5cfHDS9UWxulsv+EcmjaZ4SFSRp42CM3QNgHjOeDWEw8UaFB8Rr/wAP6ZcG+n1aJ7VntWkLReVEsksaHHmlV3kKDyy456Hs/wDhA9I/5/PEH/hR6h/8fo/4QPSP+fzxB/4Ueof/AB+gDjdBttRu/jNYan5viPUdOi0i4g+3axpq2gWQyRnYqiGIjjnLLzztJwcerVzf/CB6R/z+eIP/AAo9Q/8Aj9H/AAgekf8AP54g/wDCj1D/AOP0AYvxU/0qTwfpkfM134ltH2jqEi3Su34Bf1rvq42f4XaDca1a6lPe6/JJaRukEb69eMI2fALqxk3q20FeGAIJyDwRe/4QPSP+fzxB/wCFHqH/AMfoA6Siub/4QPSP+fzxB/4Ueof/AB+j/hA9I/5/PEH/AIUeof8Ax+gDpKK5v/hA9I/5/PEH/hR6h/8AH6s6f4S07TL+O7trnWHljztW51u8uIzkEcxySsp69wcHnqKANuiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA4HWP9C+Pfhm5k4XUNHvLKM/7aNHLj67QT+Bqn8VNQ+0T+H7K007V7qbT/ENje3JttIupo0hRtzMJEjKtgHopJ7YzXaa74cs9fk0yW6eaGbTL1Ly2lgIDK6ggqcg5VlYgj0PYgEa1AHnPxbvYtZ+E5trRZ1fWr6zs7dLi3eCQs1ynBjcBgcK3BA9a9GrK1bw9aa1qek3t88zf2TcNcwQqw8tpShQMwxklQzYwRye9atABRRRQAUUUUAFFFFABRRRQBzXj/wAWyeCfCM2tW+mnU5UmihS1E3lGRpHCD5trY5b0rNvfibZxfCQ+OtOs2u4hHGWs3l8tkdpVjdGbBwVYnPHOPfNQfGb/AJJ8v/YUsf8A0pjrz34wwyeCbDxFYRqRofiwxXNuAPltr9Jo2lX2EiKX/wB5SBQB7zeXtrp1nLd6hcw2ttCu6SaeQIiD1LHgCuK+Gvjqbxzp02rTapo3kkyMNMtVJuLNBIyoZn80g5Vc/cXrxxXeV4FbGcfsp6N5brHbG+QXskiF0S3+3NvLqCCyf3hkZGeR1oA9q0jxLoXiBpRoOtafqZhwZRZXSTeXnON20nGcHr6GoH8ZeGItvm+I9JTfG8q7r6IbkRirMPm6BlYE9AVI7Vz8Phu/l8eaBrmr+I9Haa0t7iK3trDTmtnu4nQZXLXD7lU7GGBx7Zqn8DbG2tvhybiCFUmu9RvJJ3HV2Fw6DP0VQP8A9ZoAl0X4nW/i34cz6zoV/oOn6wkLyvaajqAeK0AkKBpiuGVSBnOB94VrDxPfr8TNO8Nulq1tcaLJfySxhi3mrIiYU5xswx7Z6c15tY8fsc3iHhks7tWHdSLl8g+hrrYf+S66F/2Kkv8A6PioA6PQvEfleB7bWPF+s+H0YlhPfafdYsSfMKrskkPpgHJ+9kVrprOmSaP/AGsmpWjab5Zl+2idTDsHVt+duOOua8e8EaFca18I/BMuj6rZWeuaZPeXthb3yeZFdMJJkIZAwbA3r865K5zg5FGseILvxHp3heZ7Ww0ZYfFM1pqEjRG4spLqNXCS4DRl0aQAgkjDYySQMgHrun+IdF1ewmvtK1ewvrSAkS3FtcpJHGQMncykgYBB57Utj4g0bU7s2um6tY3dwIUnMNvcpI4iYAq+0HO0hlIPQgj1rh7/AMOXlnq3iTW9W17Tbm+uPDslvLY2Ni1sXRdxSZw00hYj5kBwODjPFa/wlsbaw+EfhlLOFYlk06GZwv8AE7oGZj7kk0AdDq2v6PoEMcuu6tY6ZHK22N7y5SEOeuAWIyakbV9NWztbttQtRbXjRrbTGddk5k+4EbOGLZGMdc8Vzer+I2t/Hw0bQtEsbvWV0z7XcXd9dC1WO28wqFDiOR2+YE427R1JycV5AZdPb4A6T/aP2ZNJj8XgSLFJvgS3+1uWCsAMpgnBAGR2oA9+0zxDoutzXEWjavYahJbECdLS5SUxE5A3BSdvQ9fQ1A3i/wANLLaxt4h0oSXjFLZTex5nYOUIQbvmO8FcDuMda5TVltD8aPBS6L5Qlj028a5FtjAstqCPOP4PM27e2c4rivCjeGV/Zz8Xm5Np5m6/XUCcb/N8x/JBz35j2+545zQB6hL4lvl+K8fheOO3+yPob6j5jK3meaJ1jAznG3DHjGc96s+Htblk8E2+r+JNT0RmEbyXF7plwTZbQxG5Xc9AAMknrmuL8Npex/GDw+mq7/ty+BVFz5n3vM+0Rbs++c1xug/aj8L/AITKlzbWtm2oSeZNeQGaBZv3vkb0Dpn5/u/MMNg9sEA930rW9K161a60PU7PUrdHMbS2dwsyKwAJUlSRnBBx7iuW8WfEWx020hHhvU9J1G9XVbWyurdbhZWgWSURtuVGyrDnGe46Vh+IfDms2I8Y60uvWNzrl14ceH7Dplk1s7bd/lzEGaRi330U8dMDpWL4wHhofDL4cfYltPM/tHTjpmwDeF3J5pGOcdN3bdtzzigD2W/vYNN025vrx9lvaxNNK391VBJP5CvO7Pxp4hsvAng3VtRME954k1i3imSWLAt7e4LsqIF28qmwAtnPJOc10nxNSV/hT4pWDO86Tc9O48psj8s1xPjwTXPw5+HQ0OaK3ml1fTPskske9I2MTbGK5GQOOM0AeqR6nYS6hcWMV9bPeWyq89usymSJW5BZc5UHsT1rMsvHPhLUr2Kz07xRot3dTNtjgg1CJ3c+gUNkmvJpp7JfgN4x02VZbLxLbfutf8990sty7ACRn4zFIOjcAJnHSuj8J6rY+MvifZaxpa6dp8elaK9q9rDqNpcTS73QgBbeRwIo9vBYjlxhe9AHWfEDxJeeFPC41LTo4JJjeW0G2dSV2yTKjHgg5wxxz1rX0/xBo2rXlzaaVq1je3NqdtxDbXKSPCckYdVJK8gjn0rhPi/eaZrvwxmS0ura/tv7WtLafyJVkUMLmMMjEHgjPI61Z8QQQ2Hxq8ArZRJbq9jqVuyxKFBiSOIomB/CDyB2oA67/hKNA/tz+xv7c03+1N237D9rj8/ON2PLzuzjnp0pJNSh1K8vNGtZdTsruOMk3S6fIqJ05SWWMwueRx83fjg48f8AG/ic618Lra/0jRNL0zQ21qD+z5WvQlyZVuvmZbdYtoYkSEjzN2NxI6ivbry0jvrOW1naZY5V2s0E7wuB7OhDKfcEGgDzu01LW7L4FXeqaTfTy6jpsl5Oj3Lm4eaOC6kJjZpNzHMaFM5yOMHNeg6ZqEGraRZ6lZtut7yBJ4m9UdQwP5GsFvDej+E/Busw6et0lm1rNJLHcXs1wo+VmYjzXbbksxOMZJyaj+FayJ8JPCwm+9/ZcBGf7pQFf0xQB1lFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBXvbCz1K38jUbSC7h3q/lzxh13KcqcHjIIBB7EU3UdL0/V7YW2rWNtfQBw4iuYVkUMOjYYEZHrVqigAqtaabY2GnrYWNlb21moIW2hiVIwCSSNoGOSST9TVmigDL0nwxoGgSySaFoem6ZJKoWR7O0jhLgdASoGRVyysLPTLUW2nWkFpAGZhFBGI1BYkscDjJJJPqTViigDJ/4RXw95l8/wDYOmb9RyL1vscebrJyfM4+fnnnPNXRp1kL+O9Fnbi7jiMCXHlL5ixkglA2MhcgHHTirNFAGVP4V8PXWkQ6Vc6Fpk2nQP5kVnJZxtDG3PzKhG0H5m5A/iPrVn+x9M/sf+yf7OtP7N8vyvsfkL5Oz+7sxtx7YxVyigDN07w3oej2c9ppOjafY21x/roba1SNJeMfMqgA8cc1dtbW3sbSK1soI7e3hQJFDEgRI1AwFCjgADsKlooAzNX8NaF4gaI69oun6mYciI3tqk3l5xnbuBxnA6egrE8YeDW1nS9MtNCisrIW2t2+pzKV8tXCS75D8qnLtyeepPJrrqKAM/S/D+jaG050TSbHTjcENMbS2SLzSM4LbQM9T19TXMeCPh3ZaHoFjF4h03Sr/VbK5nmgvBAsrQh53lTY7qGUjcOmMHOPWu3ooAr/AGCzOpDUDaQfbRCYBc+WPMEZO4pu67cgHHTIqFdD0lNF/sdNLsl0zYU+wi3QQ7SckeXjbjPOMVeooAoaToOkaDA8OhaVZabFI2947O3SFWbGMkKBk4qvD4R8N28s8lv4f0uJ7iRZZmSyjUyurblZiByQ3zAnkHmteigCO4t4ru1lt7hBJDMhjkQ9GUjBH5VzXg/wm+j+DdL0PxElnqT6PKfsczJ5mERm8h8Mo2SKhA46EHB5rqaKAOS8eeCk8UeHtQj0tLG11e5jjX7VPBnz0jcOIJWXDGJiu0jJwCTg9DBJ4f8AEGv+MdC1jX4NL0yHQ3llhSxupLqWd5IzGVLtFHsTByQA2446YrtKKAM4eHtFWyks10iwFrJP9peAWybHlyG8wrjBbIB3dcgVZlsLOe+t7ye0glurUOIJ3jBeEOAG2seVyAM464FWKKAMY+DvDLXlxeN4c0k3N0czzmxi3zHcGyzbct8wDc9wDWzRRQBgeN9Fv/EfgzUNF0ueK3l1CMW0k0pICRMQJSMA5bYWAHTJGSK2bO1hsLGC0tU2QW8axRqP4VUYA/IVNRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf/Z)
A NUE of 13 to 18 kg lint/kg of N is recommended for irrigated cotton, with values below 13 indicating that too much N was applied and values above 18 indicating that insufficient N was applied (Rochester 2014).
To evaluate for statistical differences between treatment means, ANOVA was conducted on the experimental data using Genstat 16.0. Where significant statistical differences, at the α = 0.05 and lower level were identified, Fisher’s least significant differences (LSD) were calculated from which the means differences were derived. For ease of interpretation, non-significant results were designated as n.s. Means, with the same letter were not significantly different.