{"id":358,"date":"2018-01-23T10:00:48","date_gmt":"2018-01-23T12:00:48","guid":{"rendered":"http:\/\/pressreleases.scielo.org\/en\/?p=358"},"modified":"2023-03-28T13:50:57","modified_gmt":"2023-03-28T16:50:57","slug":"aquacrop-model-is-efficient-in-simulation-of-soybean-yield-in-the-matopiba-region-in-brazil","status":"publish","type":"post","link":"https:\/\/pressreleases.scielo.org\/en\/2018\/01\/23\/aquacrop-model-is-efficient-in-simulation-of-soybean-yield-in-the-matopiba-region-in-brazil\/","title":{"rendered":"AquaCrop model is efficient in simulation of soybean yield in the Matopiba region, in Brazil"},"content":{"rendered":"

By Roberta Ara\u00fajo e Silva, Doctorate in Meteorology and Technique of the Laboratory of Meteorology and Climatology – LABMET, Federal University of Tocantins (UFT), Palmas, TO, Brasil<\/strong>\"\"<\/a><\/p>\n

Study \u201cCalibration and validation of the AquaCrop model for the soybean crop grown under different levels of irrigation in the Motopiba region, Brazil\u201d, published in Ci\u00eancia Rural (vol. 48, no. 1), describes the cultivar as a experiment the crop to seven irrigation treatments during the dry rainy seasons. The model was then fed with soybean yield data collected directly in the field. To evaluate the performance of the model, the researchers used the following statistical parameters: prediction error, Nash-Sutcliffe efficiency index, determination coefficient, absolute mean error, root mean square error and Willmott index. Some studies, such as that of Hsaio, et al<\/em>. (2009), Iqbal, et al<\/em>. (2014) and Steduto, et al<\/em>. (2009) were used as references.<\/p>\n

The statistical analyzes of the model presented acceptable errors in the prediction of soybean yield under tropical climatic conditions, indicating that it can be used for this type of simulation. According to researcher Roberta Ara\u00fajo e Silva, these results can be used to quantify future soybeans productivity in the study region, and can also provide useful information to improve the management of the region’s water resources, avoiding wastes with time and money. “With this model, it is possible to know the response of the crop to the water availability of each location and its variation throughout the year without the need for a field experiment\u201d, she says.<\/p>\n

The choice of testing a cultivar in the Matopiba region was not random, since the region has been highlighting in recent years the production of grains for export such as soybeans, whose crop is vulnerable to climatic inclement weather. In the harvest of 2015\/2016, soon after a record breaking in the production of the yield 2014\/2015, this region suffered a drastic fall in the production due to the inclement weather. “Surveys like ours allow the simulation of grain yields in response to climate and soil variability and can assist farmers in this region in decision-making,” comments Roberta.<\/p>\n

References<\/h3>\n

HSIAO, T. C., et al<\/em>. AquaCrop-the FAO crop model to simulate yield response to water: III. Parameterization and testing for maize. Agronomy Journal<\/em> [online]. 2009, vol. 101, no. 3, pp. 448-459, ISSN: 1435-0645 [viewed 18 January 2018]. DOI: 10.2134\/agronj2008.0218s<\/a>. Available from: https:\/\/dl.sciencesocieties.org\/publications\/aj\/abstracts\/101\/3\/ 448?access=0&view=pdf<\/a><\/p>\n

IQBAL, M. A., et al<\/em>. Evaluation of the FAO AquaCropmodel for winter wheat on the North China Plain under deficit irrigation from field experiment to regional yield simulation. Agricultural Water Management<\/em> [online]. 2014, vol. 135, pp. 61-72, ISSN: 0378-3774 [viewed 18 January 2018]. DOI: 10.1016\/j.agwat.2013.12.012<\/a>. Available from: http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0378377413003557?via%3Dihub<\/a><\/p>\n

STEDUTO, P., et al<\/em>. AquaCrop-The FAO crop model to simulate yield response to water: I. Concepts and under lying principles. Agronomy Journal<\/em> [online]. 2009, vol. 101, pp. 426-437, ISSN: 1435-0645 [viewed 18 January 2018]. DOI: 10.2134\/agronj2008.0139s<\/a>. Available from: https:\/\/dl.sciencesocieties.org\/publications\/aj\/abstracts\/ 101\/3\/426?access=0&view=pdf<\/a><\/p>\n

To read the article, access it:<\/h3>\n

SILVA, V. P. R., et al<\/em>. Calibration and validation of the AquaCrop model for the soybean crop grown under different levels of irrigation in the Motopiba region, Brazil. Cienc. Rural<\/em> [online]. 2018, <\/em>vol. 48, no. 1, e20161118, ISSN: 1678-4596 [viewed 18 January 2018]. DOI: 10.1590\/0103-8478cr20161118<\/a>. Available from: http:\/\/ref.scielo.org\/7jbrt6<\/a><\/p>\n

External link<\/h3>\n

Ci\u00eancia Rural \u2013 CR: <http:\/\/www.scielo.br\/cr<\/a>><\/p>\n","protected":false},"excerpt":{"rendered":"

Is there an efficient model capable of estimating the yield of soybean cultivars in a particular growing region? Based on this question, researchers from the Federal University of Campina Grande, the Federal University of Tocantins and the Federal University of Pernambuco, Brazil, tested the use of the AquaCrop model to simulate the productivity of soybean cultivar TMG 1288 RR in the Matopiba region, confirming its efficiency. …<\/span> Read More →<\/span><\/a><\/span><\/p>\n","protected":false},"author":154,"featured_media":52,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[3,20,12],"tags":[192,21],"class_list":["post-358","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-agricultural-sciences","category-cr","category-press-releases","tag-agronomy","tag-ciencia-rural"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/posts\/358","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/users\/154"}],"replies":[{"embeddable":true,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/comments?post=358"}],"version-history":[{"count":3,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/posts\/358\/revisions"}],"predecessor-version":[{"id":361,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/posts\/358\/revisions\/361"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/media\/52"}],"wp:attachment":[{"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/media?parent=358"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/categories?post=358"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/tags?post=358"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}