{"id":70645,"date":"2022-07-28T17:43:57","date_gmt":"2022-07-28T14:43:57","guid":{"rendered":"https:\/\/eos.com\/?page_id=70645"},"modified":"2024-12-17T16:02:31","modified_gmt":"2024-12-17T13:02:31","slug":"yield-prediction","status":"publish","type":"page","link":"https:\/\/eos.com\/products\/crop-monitoring\/custom-solutions\/yield-prediction\/","title":{"rendered":"Crop Yield Prediction"},"content":{"rendered":"","protected":false},"excerpt":{"rendered":"","protected":false},"author":43,"featured_media":0,"parent":44505,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/page-crop-yield-prediction.php","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"class_list":["post-70645","page","type-page","status-publish","hentry"],"acf":{"":null,"media":{"alt":"satellite collecting yield data","image":91605,"image_tablet":70925,"image_mobile":91609},"content":[{"acf_fc_layout":"title","title":"Crop Yield Prediction"},{"acf_fc_layout":"description","description":"EOSDA team of data scientists and engineers has developed effective techniques for crop yield estimation using remote sensing and machine learning models. We\u2019re relying on earth observation data retrieved from satellites to cover areas ranging from individual farms to regions."},{"acf_fc_layout":"buttons","buttons":[{"acf_fc_layout":"modal_button","style":"btn-primary1","label":"Get Yield Prediction","trigger":"#modalSalesLocal","click_event":"dataLayer.push({'event':'EOSCOM', 'eventCategory':'EosComEvents', 'eventAction':'contact_us_button', 'eventLabel':'first_section', 'eventContent':'', 'eventPosition':'yield_prediction', 'eventContext':'get yield prediction'});\r\ndataLayer.push({'event':'EOSCOM','eventCategory':'EosComEvents', 'eventAction':'Contact Sales Button Click', 'eventLabel':'Yield Prediction First Section'});","autocomplete_forms":"sessionStorage.setItem('#modalSalesLocal',\r\n JSON.stringify({\r\n 'modal-autocomplete': { 'subject' : 'Talk to expert', 'product' : 'Yield prediction' },\r\n 'events-form' : {'eventLabel':'first_section', 'eventPosition' : 'yield_prediction'} \r\n }) \r\n);","hide_button":false}]}],"what_we_offer":{"":null,"pre_title":"Numbers","title":"Crop Yield Prediction in Numbers","cards":[{"title":"Accuracy","data":" up to 95%<\/span>","description":"Accuracy of yield estimated depends on the quality of statistical data and can vary from 85% to 95%."},{"title":"Forecasts","data":"Up to 3<\/span> months ahead ","description":"Current season yield forecasts up to 3 months in advance."},{"title":"Crop types","data":"100 +<\/span>","description":"Yield predicted for over 100 crop types."},{"title":"Project speed","data":"up to 14<\/span> days","description":"We\u2019ll produce a 95% accurate yield forecast in two weeks or less, depending on the complexity of the project."},{"title":"Entries per crop","data":"0<\/span> to 100<\/span> fields","description":"WOFOST yield estimation model requires no data at all. "},{"title":"Data Sources","data":"10 +<\/span>","description":"We make sure the forecasts are based on the most comprehensive data analysis."}],"buttons":[{"acf_fc_layout":"modal_button","style":"btn-primary1","label":"Get Yield Prediction","trigger":"#modalSalesLocal","click_event":"dataLayer.push({'event':'EOSCOM', 'eventCategory':'EosComEvents', 'eventAction':'contact_us_button', 'eventLabel':'numbers_section', 'eventContent':'', 'eventPosition':'yield_prediction', 'eventContext':'get yield prediction'});\r\ndataLayer.push({'event':'EOSCOM','eventCategory':'EosComEvents', 'eventAction':'Contact Sales Button Click', 'eventLabel':'Yield Prediction Numbers Section'});","autocomplete_forms":"sessionStorage.setItem('#modalSalesLocal',\r\n JSON.stringify({\r\n 'modal-autocomplete': { 'subject' : 'Talk to expert', 'product' : 'Yield prediction' },\r\n 'events-form' : {'eventLabel':'numbers_section', 'eventPosition' : 'yield_prediction'} \r\n }) \r\n);","hide_button":false}]},"video":{"":null,"title":"Watch video about our yield prediction solution","description":"Global food security depends on the efficiency of food management practices, such as yield prediction, which allows farmers to raise crops in a more sustainable way. EOSDA\u2019s yield forecast solution \u2013 developed based on the latest technological advances in machine learning and geospatial analytics \u2013 provides farmers, agri holdings, food security companies and other decision-makers with crucial data needed for sustainable and profitable crop production.","video_alt":"video about our yield prediction solution","video_id":"okclK7AzCBQ","video_data":{"":null,"width":"1920","height":"1080","duration":"PT00H2M46S","uploaddate":"2023-05-16T12:02:58+03:00","thumbnailurl":"https:\/\/i.ytimg.com\/vi\/okclK7AzCBQ\/maxresdefault.jpg","embedurl":"https:\/\/www.youtube.com\/watch?v=okclK7AzCBQ"}},"what_you_get":{"":null,"pre_title":"Benefits","title":"Yield Estimation Benefits","cards":[{"image":70656,"alt":"information for yield forecast","list":[{"text":"Increased speed of decision-making related to harvesting, storing, and transporting operations."},{"text":"Data on crop profitability in your area of interest based on yield estimation."},{"text":"Opportunity to strengthen global food security by introducing crop yield forecasting to developing countries - helping them to prevent famine, boost local economies, and implement sustainable agricultural practices."}]},{"image":70658,"alt":"crop yield simulations","list":[{"text":"Improved understanding of the agricultural market and better-informed decisions on the management of stocks, imports, and exports, in accordance with CAP and other similar policies."},{"text":"A much better understanding of cumulative effects of hostile field conditions (pests, diseases, nutrient deficiencies, and others) on crop development. "}]}],"buttons":[{"acf_fc_layout":"modal_button","style":"btn-primary1","label":"Get Yield Prediction","trigger":"#modalSalesLocal","click_event":"dataLayer.push({'event':'EOSCOM', 'eventCategory':'EosComEvents', 'eventAction':'contact_us_button', 'eventLabel':'benefits_section', 'eventContent':'', 'eventPosition':'yield_prediction', 'eventContext':'get yield prediction'});\r\ndataLayer.push({'event':'EOSCOM','eventCategory':'EosComEvents', 'eventAction':'Contact Sales Button Click', 'eventLabel':'Yield Prediction Benefits Section'});","autocomplete_forms":"sessionStorage.setItem('#modalSalesLocal',\r\n JSON.stringify({\r\n 'modal-autocomplete': { 'subject' : 'Talk to expert', 'product' : 'Yield prediction' },\r\n 'events-form' : {'eventLabel':'benefits_section', 'eventPosition' : 'yield_prediction'} \r\n }) \r\n);","hide_button":false}],"background_image":69871},"prediction_models":[{"acf_fc_layout":"intro","pre_title":"Methodology ","title":"Our Approach","description":"For maximum efficiency and accuracy of crop yield forecasting, we fuse two different types of yield prediction models - biophysical and statistical. This \"hybrid\" approach allows us to take on more complex projects. "},{"acf_fc_layout":"two_models","cards":[{"title":"Biophysical yield prediction model","list":[{"text":"Collect data (weather parameters, soil analysis, crop state, phenological data, etc.). "},{"text":"Calibrate the model and carry out the LAI assimilation to ensure accuracy of a crop yield forecast in the absence of statistical data and to increase the variability of values."},{"text":"Simulate the biological productivity parameters (TAGP, WSO, relative soil moisture, total water consumption, and others) to estimate yield."},{"text":"Update the data once every 14 days to increase the accuracy. This has to do with weather updates. "}]},{"title":"Statistical yield prediction model","list":[{"text":"Collecting data to create a crop yield prediction dataset and combining it with possible predictors (rainfall, temperature, humidity, soil type, and others)."},{"text":"Picking the right ML model for the project - e.g. Linear regression, Random Forest, LightGBM, XGBoost, CatBoost, to name a few."},{"text":"Adjusting the model to answer the specific needs of the project in question for best results."}]}]},{"acf_fc_layout":"fusing_models","title":"Model fusion stage","description":"The fusion stage is necessary if we want to achieve the highest possible accuracy of 95%<\/span>. We fuse the biophysical yield prediction model with the statistical model described above. ","start_element":"EOSDA Crop Modeling + LAI Assimilation","list":[{"item":"Ensemble of model scenarios"},{"item":"Asquisition 1"},{"item":"Asquisition 2"},{"item":"Asquisition 3Selection of most likely scenario and re-initalization of the modelled ststem state with the scenario<\/span>"},{"item":"Asquisition 4Observations of LAI<\/span>"},{"item":"Asquisition 5"},{"item":"Harvest"}]},{"acf_fc_layout":"lai_assimilation","title":"Applying LAI assimilation allowed us to achieve the 95% accuracy in 30% of the fields. For the fields marked in red, the accuracy of less than 80% was achieved, while the crop yield forecast accuracy for the green-marked fields exceeded the 80% mark. ","cards":[{"image":70834,"alt":"EOSDA Crop Modeling accuracy","caption":"EOSDA Crop Modeling"},{"image":70836,"alt":"EOSDA Crop Modeling + LAI assimilation accuracy","caption":"EOSDA Crop Modeling + LAI Assimilation"}]},{"acf_fc_layout":"buttons","buttons":[{"acf_fc_layout":"modal_button","style":"btn-primary1","label":"Get Yield Prediction","trigger":"#modalSalesLocal","click_event":"dataLayer.push({'event':'EOSCOM', 'eventCategory':'EosComEvents', 'eventAction':'contact_us_button', 'eventLabel':'methodology_section', 'eventContent':'', 'eventPosition':'yield_prediction', 'eventContext':'get yield prediction'});\r\ndataLayer.push({'event':'EOSCOM','eventCategory':'EosComEvents', 'eventAction':'Contact Sales Button Click', 'eventLabel':'Yield Prediction Methodology Section'});","autocomplete_forms":"sessionStorage.setItem('#modalSalesLocal',\r\n JSON.stringify({\r\n 'modal-autocomplete': { 'subject' : 'Talk to expert', 'product' : 'Yield prediction' },\r\n 'events-form' : {'eventLabel':'methodology_section', 'eventPosition' : 'yield_prediction'} \r\n }) \r\n);","hide_button":false}]}],"success_stories":{"":null,"pre_title":"Use Cases","title":"Our Success Stories ","additional_title":"Yield forecast for a large agroholding in Ukraine","description":"In 2020, we implemented a yield prediction project for 6 major crops: Winter Barley, Winter Rapeseed, Winter\/Spring Wheat, Sunflower, Soy, and Maize. ","content":{"intro_text":"Two different reports were generated:","":null,"list_reports":[{"report":"45 days prior to the harvest"},{"report":"2 weeks prior to the harvest."}],"list_accuracy":[{"accuracy":"less than 80% accuracy"},{"accuracy":"more than 80% accuracy"}],"figure_list":[{"image":70830,"alt":"WOFOST Yield prediction ","caption":"WOFOST Yield prediction "},{"image":70832,"alt":"WOFOST (inputs\/outputs) + LAI (Sentinel-2)","caption":"EOSDA Yield prediction Machine Learning Model WOFOST (inputs\/outputs) + LAI (Sentinel-2)"}],"accuracy_table":"
\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
<\/th>\r\nAccuracy (Wofost)<\/th>\r\nAccuracy (Wofost + Lai)<\/th>\r\n<\/tr>\r\n<\/thead>\r\n
Maize<\/td>\r\n0.75<\/td>\r\n0.91<\/td>\r\n<\/tr>\r\n
Soy<\/td>\r\n0.78<\/td>\r\n0.86<\/td>\r\n<\/tr>\r\n
Sunflower<\/td>\r\n0.71<\/td>\r\n0.88<\/td>\r\n<\/tr>\r\n
Winter barley<\/td>\r\n0.53<\/td>\r\n0.82<\/td>\r\n<\/tr>\r\n
Winter wheat<\/td>\r\n0.75<\/td>\r\n0.92<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>","percent_table":"
\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
<\/th>\r\nNumber of Fields<\/th>\r\n<\/tr>\r\n<\/thead>\r\n
Crop \/ Accuracy<\/span><\/span><\/td>\r\n<70%<\/td>\r\n70-75%<\/td>\r\n75-80%<\/td>\r\n80-85%<\/td>\r\n85-90%<\/td>\r\n>90%<\/td>\r\n<\/tr>\r\n
Winter barley<\/td>\r\n27<\/td>\r\n7<\/td>\r\n5<\/td>\r\n22<\/td>\r\n23<\/td>\r\n52<\/td>\r\n<\/tr>\r\n
Winter wheat<\/td>\r\n33<\/td>\r\n17<\/td>\r\n19<\/td>\r\n21<\/td>\r\n19<\/td>\r\n102<\/td>\r\n<\/tr>\r\n
Winter rapeseed<\/td>\r\n26<\/td>\r\n6<\/td>\r\n20<\/td>\r\n14<\/td>\r\n27<\/td>\r\n22<\/td>\r\n<\/tr>\r\n
Sunflower<\/td>\r\n12<\/td>\r\n11<\/td>\r\n12<\/td>\r\n14<\/td>\r\n19<\/td>\r\n22<\/td>\r\n<\/tr>\r\n
Soy<\/td>\r\n28<\/td>\r\n22<\/td>\r\n29<\/td>\r\n58<\/td>\r\n37<\/td>\r\n86<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>","description_second":"By improving the model with LAI assimilation, which was developed by the team, we managed to increase the accuracy of yield estimation in 30% of the fields compared to the traditional WOFOST approach. ","intro_text_second":"The table below shows the correlation between the accuracy of yield estimation, the target crop and the number of fields. For example, the predicted yield of winter barley was more than 90% accurate for 52 fields."}},"challenge_1":{"":null,"title":"Crop yield forecasting for a Canadian insurance company","input_data":[{"data":"Goal:<\/span> Reliable predicted yield data on every customer to reduce insurance risks."},{"data":"Input data:<\/span> Over 100 fields on 20 farms. "}],"challenge_pre_title":"Task 1. ","challenge_title":"Estimating average yield for 6 major crop types growing in every field on all 20 farms and comparing it against the actual yield report.","challenge_intro":"yield estimation model vs actual yield by crop type given in %","crop_type_list":[{"icon":70673,"title":"Canola","data":"> 98,03"},{"icon":70674,"title":"Corn","data":"> 87, 59"},{"icon":70684,"title":"Peas","data":"> 76,25"},{"icon":70675,"title":"Soybean","data":"> 95,94"},{"icon":70676,"title":"Sunflower","data":"> 98,21"},{"icon":70677,"title":"Wheat","data":"> 98,63"}],"graph_image":70672,"graph_alt":"","graph_table":"
\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
Crop<\/th>\r\nModelled Yield<\/th>\r\nActual Yield<\/th>\r\n<\/tr>\r\n<\/thead>\r\n
Canola, lbm\/ac<\/td>\r\n41,81<\/td>\r\n41,00<\/td>\r\n<\/tr>\r\n
Corn, BPA<\/td>\r\n123,65<\/td>\r\n110,00<\/td>\r\n<\/tr>\r\n
Peas, q\/ac<\/td>\r\n30,94<\/td>\r\n25,00<\/td>\r\n<\/tr>\r\n
Soybean, BPA<\/td>\r\n22,89<\/td>\r\n22,00<\/td>\r\n<\/tr>\r\n
Sunflower, lbm\/ac<\/td>\r\n1767,73<\/td>\r\n1800,00<\/td>\r\n<\/tr>\r\n
Wheat, BPA<\/td>\r\n53,72<\/td>\r\n53,00<\/td>\r\n<\/tr>\r\n
Grand Total<\/td>\r\n95,6<\/td>\r\n94,47<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>"},"challenge_2":{"":null,"challenge_pre_title":" Task 2.","challenge_title":"Estimating yield 14 days prior to the 2020 harvest.","challenge_intro":"yield estimation model vs actual yield by crop type given in %","crop_type_list":[{"icon":70673,"title":"Canola","data":"> 96,96"},{"icon":70674,"title":"Corn","data":"> 91,69"},{"icon":70677,"title":"Oats","data":"> 99,98"},{"icon":70677,"title":"Rye Fall","data":"> 85,85"},{"icon":70676,"title":"Sunflowers Confects","data":"> 85,36"},{"icon":70676,"title":"Sunflower Oils","data":"> 98,06"},{"icon":70677,"title":"Wheat","data":"> 94,95"}],"graph_table":"
\r\n
\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
Crop<\/th>\r\nModelled Yield<\/th>\r\nActual Yield (Farm 4)<\/th>\r\n<\/tr>\r\n<\/thead>\r\n
Canola<\/td>\r\n40,19<\/td>\r\n39,00<\/td>\r\n<\/tr>\r\n
Corn<\/td>\r\n119,14<\/td>\r\n110,00<\/td>\r\n<\/tr>\r\n
Oats<\/td>\r\n125,03<\/td>\r\n125,00<\/td>\r\n<\/tr>\r\n
Rye Fall<\/td>\r\n64,39<\/td>\r\n75,00<\/td>\r\n<\/tr>\r\n
Sunflowers Confects<\/td>\r\n2063,60<\/td>\r\n1800,00<\/td>\r\n<\/tr>\r\n
Sunflower Oils<\/td>\r\n1834,19<\/td>\r\n1800,00<\/td>\r\n<\/tr>\r\n
Wheat<\/td>\r\n61,73<\/td>\r\n65,00<\/td>\r\n<\/tr>\r\n
Grand Total<\/td>\r\n584,34<\/td>\r\n528,00<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>\r\n<\/div>"},"challenge_3":{"":null,"challenge_pre_title":"Task 3.","challenge_title":"Providing the client with the crop yield forecasting data to enable more efficient planning of crop rotation and, as a result, significantly reduce insurance risks.","challenge_intro":"The graph shows the predicted yield for target crops in 3 selected fields in bushel\/ha.","graph_table":"
\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
Field name<\/th>\r\nCanola<\/th>\r\nCorn<\/th>\r\nSoybean<\/th>\r\nSunflower Oils<\/th>\r\nWheat<\/th>\r\n<\/tr>\r\n<\/thead>\r\n
SE-2-6-28-W1<\/td>\r\n58,68<\/td>\r\n194,33<\/td>\r\n41,45<\/td>\r\n2208,85<\/td>\r\n72,49<\/td>\r\n<\/tr>\r\n
SW-36-7-28-W1<\/td>\r\n30,91<\/td>\r\n169,49<\/td>\r\n14,42<\/td>\r\n1146,91<\/td>\r\n46,24<\/td>\r\n<\/tr>\r\n
W-34-5-27-W1<\/td>\r\n38,77<\/td>\r\n151,58<\/td>\r\n24,71<\/td>\r\n1476,83<\/td>\r\n59,82<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>","conclusion_title":"The Jackknife resampling technique was used. Namely, by systematically omitting every observation from a dataset we calculated the estimate and then discovered the average of the calculations. To exclude climatic and technological factors, we only used the data for the past 6 years.","conclusion_text":"The harvest period for the target crops in Canada usually lasts from August till September. Knowing this, we were able to forecast yield two months before the harvest, achieving an accuracy of over 82%. The accuracy was steadily increasing as the harvest was approaching until it reached 90% just two weeks prior to the harvest, as had been expected.","buttons":[{"acf_fc_layout":"modal_button","style":"btn-primary1","label":"Get Yield Prediction","trigger":"#modalSalesLocal","click_event":"dataLayer.push({'event':'EOSCOM', 'eventCategory':'EosComEvents', 'eventAction':'contact_us_button', 'eventLabel':'usecases_section', 'eventContent':'', 'eventPosition':'yield_prediction', 'eventContext':'get yield prediction'});\r\ndataLayer.push({'event':'EOSCOM','eventCategory':'EosComEvents', 'eventAction':'Contact Sales Button Click', 'eventLabel':'Yield Prediction Use Cases Section'});","autocomplete_forms":"sessionStorage.setItem('#modalSalesLocal',\r\n JSON.stringify({\r\n 'modal-autocomplete': { 'subject' : 'Talk to expert', 'product' : 'Yield prediction' },\r\n 'events-form' : {'eventLabel':'usecases_section', 'eventPosition' : 'yield_prediction'} \r\n }) \r\n);","hide_button":false}]},"solutions_request":{"":null,"title":"Custom Solutions for Agri Purposes","cards":[{"image":91008,"alt":"Advanced Soil Moisture image","page_link":"https:\/\/eos.com\/products\/crop-monitoring\/custom-solutions\/soil-moisture-analytics\/","title":"Advanced Soil Moisture"},{"image":91011,"alt":"Harvest Monitoring image","page_link":"https:\/\/eos.com\/products\/crop-monitoring\/custom-solutions\/harvest-monitoring\/","title":"Harvest Monitoring"},{"image":91010,"alt":"Field Boundaries Detection image","page_link":"https:\/\/eos.com\/products\/crop-monitoring\/custom-solutions\/field-boundaries-detection\/","title":"Field Boundaries Detection"},{"image":91009,"alt":"Crop Classification image","page_link":"https:\/\/eos.com\/products\/crop-monitoring\/custom-solutions\/crops-classification\/","title":"Crop Classification"}]},"eos_banner":{"":null,"decorate_image":70828,"logo_image":71371,"logo_alt":"EOSDA logo","text":"EOSDA is a leader in the application of earth observation technologies for business and environmental purposes in 22+ industries, making a special emphasis on agriculture and forestry. So far, over 900,000 customers worldwide have benefited from the EOSDA satellite monitoring products. We have launched our own multi-purpose satellite that provides imagery in 11 spectral channels for agronomists all over the globe. You can task us with crop yield estimation for an area as small as a field and as large as a country. Using 10 separate sources of data along with trained neural network models, our team of RnD scientists will help your business thrive sustainably."},"left_image_alt":"our expert","left_image":72414,"right_image_alt":"satellite","right_image":72415,"title":"We\u2019re here to help!","subtitle":"Improve your farming experience thanks to a remotely managed agro team of your dream","buttons":[{"acf_fc_layout":"modal_button","style":"btn-primary1","label":"Schedule a demo","trigger":"#modalSalesLocal","click_event":"dataLayer.push({'event':'EOSCOM', 'eventCategory':'EosComEvents', 'eventAction':'demo_request_button', 'eventLabel':'help_section', 'eventContent':'', 'eventPosition':'yield_prediction', 'eventContext':''});\r\ndataLayer.push({'event':'EOSCOM','eventCategory':'EosComEvents', 'eventAction':'CM Schedule a Demo Button Click', 'eventLabel':'Yield Prediction Ready Section'});","autocomplete_forms":"sessionStorage.setItem('#modalSalesLocal',\r\n JSON.stringify({\r\n 'modal-autocomplete': { 'subject' : 'Demo Presentation', 'product' : 'Yield prediction' },\r\n 'events-form' : {'eventLabel':'help_section', 'eventPosition' : 'yield_prediction'} \r\n }) \r\n);","hide_button":false}],"crop_tables":[{"acf_fc_layout":"table_crop","table":"
\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
<\/th>\r\nAccuracy (Wofost)<\/th>\r\nAccuracy (Wofost + Lai)<\/th>\r\n<\/tr>\r\n<\/thead>\r\n
<\/td>\r\nMaize<\/td>\r\n<\/tr>\r\n
Maize<\/td>\r\n0.75<\/td>\r\n0.91<\/td>\r\n<\/tr>\r\n
<\/td>\r\nSoy<\/td>\r\n<\/tr>\r\n
Soy<\/td>\r\n0.78<\/td>\r\n0.86<\/td>\r\n<\/tr>\r\n
<\/td>\r\nSunflower<\/td>\r\n<\/tr>\r\n
Sunflower<\/td>\r\n0.71<\/td>\r\n0.88<\/td>\r\n<\/tr>\r\n
<\/td>\r\nWinter barley<\/td>\r\n<\/tr>\r\n
Winter barley<\/td>\r\n0.53<\/td>\r\n0.82<\/td>\r\n<\/tr>\r\n
<\/td>\r\nWinter wheat<\/td>\r\n<\/tr>\r\n
Winter wheat<\/td>\r\n0.75<\/td>\r\n0.92<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>"},{"acf_fc_layout":"table_crop_percent","table":"
\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
<\/th>\r\nNumber of Fields<\/th>\r\n<\/tr>\r\n<\/thead>\r\n
<\/td>\r\nCrop \/ Accuracy<\/td>\r\n<\/tr>\r\n
Crop \/ Accuracy<\/span><\/span><\/td>\r\n<70%<\/td>\r\n70-75%<\/td>\r\n75-80%<\/td>\r\n80-85%<\/td>\r\n85-90%<\/td>\r\n>90%<\/td>\r\n<\/tr>\r\n
<\/td>\r\nWinter barley\r\n\r\n
\r\n\r\nNumber of Fields<\/cite><\/td>\r\n<\/tr>\r\n
Winter barley<\/td>\r\n27<\/td>\r\n7<\/td>\r\n5<\/td>\r\n22<\/td>\r\n23<\/td>\r\n52<\/td>\r\n<\/tr>\r\n
<\/td>\r\nWinter wheat\r\n\r\n
\r\n\r\nNumber of Fields<\/cite><\/td>\r\n<\/tr>\r\n
Winter wheat<\/td>\r\n33<\/td>\r\n17<\/td>\r\n19<\/td>\r\n21<\/td>\r\n19<\/td>\r\n102<\/td>\r\n<\/tr>\r\n
<\/td>\r\nWinter rapeseed\r\n\r\n
\r\n\r\nNumber of Fields<\/cite><\/td>\r\n<\/tr>\r\n
Winter rapeseed<\/td>\r\n26<\/td>\r\n6<\/td>\r\n20<\/td>\r\n14<\/td>\r\n27<\/td>\r\n22<\/td>\r\n<\/tr>\r\n
<\/td>\r\nSunflower\r\n\r\n
\r\n\r\nNumber of Fields<\/cite><\/td>\r\n<\/tr>\r\n
Sunflower<\/td>\r\n12<\/td>\r\n11<\/td>\r\n12<\/td>\r\n14<\/td>\r\n19<\/td>\r\n22<\/td>\r\n<\/tr>\r\n
<\/td>\r\nSoy\r\n\r\n
\r\n\r\nNumber of Fields<\/cite><\/td>\r\n<\/tr>\r\n
Soy<\/td>\r\n28<\/td>\r\n22<\/td>\r\n29<\/td>\r\n58<\/td>\r\n37<\/td>\r\n86<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>"},{"acf_fc_layout":"table_crop_total","table":"
\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
Crop<\/th>\r\nModelled Yield<\/th>\r\nActual Yield<\/th>\r\n<\/tr>\r\n<\/thead>\r\n
<\/td>\r\nCanola, lbm\/ac<\/td>\r\n<\/tr>\r\n
Canola, lbm\/ac<\/td>\r\n41,81<\/td>\r\n41,00<\/td>\r\n<\/tr>\r\n
<\/td>\r\nCorn, BPA<\/td>\r\n<\/tr>\r\n
Corn, BPA<\/td>\r\n123,65<\/td>\r\n110,00<\/td>\r\n<\/tr>\r\n
<\/td>\r\nPeas, q\/ac<\/td>\r\n<\/tr>\r\n
Peas, q\/ac<\/td>\r\n30,94<\/td>\r\n25,00<\/td>\r\n<\/tr>\r\n
<\/td>\r\nSoybean, BPA<\/td>\r\n<\/tr>\r\n
Soybean, BPA<\/td>\r\n22,89<\/td>\r\n22,00<\/td>\r\n<\/tr>\r\n
<\/td>\r\nSunflower, lbm\/ac<\/td>\r\n<\/tr>\r\n
Sunflower, lbm\/ac<\/td>\r\n1767,73<\/td>\r\n1800,00<\/td>\r\n<\/tr>\r\n
<\/td>\r\nWheat, BPA<\/td>\r\n<\/tr>\r\n
Wheat, BPA<\/td>\r\n53,72<\/td>\r\n53,00<\/td>\r\n<\/tr>\r\n
<\/td>\r\nGrand Total<\/td>\r\n<\/tr>\r\n
Grand Total<\/td>\r\n95,6<\/td>\r\n94,47<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>"},{"acf_fc_layout":"table_crop_total","table":"
\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
Crop<\/th>\r\nModelled Yield<\/th>\r\nActual Yield (Farm 4)<\/th>\r\n<\/tr>\r\n<\/thead>\r\n
<\/td>\r\nCanola<\/td>\r\n<\/tr>\r\n
Canola<\/td>\r\n40,19<\/td>\r\n39,00<\/td>\r\n<\/tr>\r\n
<\/td>\r\nCorn<\/td>\r\n<\/tr>\r\n
Corn<\/td>\r\n119,14<\/td>\r\n110,00<\/td>\r\n<\/tr>\r\n
<\/td>\r\nOats<\/td>\r\n<\/tr>\r\n
Oats<\/td>\r\n125,03<\/td>\r\n125,00<\/td>\r\n<\/tr>\r\n
<\/td>\r\nRye Fall<\/td>\r\n<\/tr>\r\n
Rye Fall<\/td>\r\n64,39<\/td>\r\n75,00<\/td>\r\n<\/tr>\r\n
<\/td>\r\nSunflowers Confects<\/td>\r\n<\/tr>\r\n
Sunflowers Confects<\/td>\r\n2063,60<\/td>\r\n1800,00<\/td>\r\n<\/tr>\r\n
<\/td>\r\nSunflower Oils<\/td>\r\n<\/tr>\r\n
Sunflower Oils<\/td>\r\n1834,19<\/td>\r\n1800,00<\/td>\r\n<\/tr>\r\n
<\/td>\r\nWheat<\/td>\r\n<\/tr>\r\n
Wheat<\/td>\r\n61,73<\/td>\r\n65,00<\/td>\r\n<\/tr>\r\n
<\/td>\r\nGrand Total<\/td>\r\n<\/tr>\r\n
Grand Total<\/td>\r\n584,34<\/td>\r\n528,00<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>"},{"acf_fc_layout":"table_crop_rate","table":"\r\n\r\n\r\n\r\n\r\n\r\n\r\n
Field name<\/th>\r\nCanola<\/th>\r\nCorn<\/th>\r\nSoybean<\/th>\r\nSunflower Oils<\/th>\r\nWheat<\/th>\r\n<\/tr>\r\n<\/thead>\r\n
SE-2-6-28-W1<\/td>\r\n58,68<\/td>\r\n194,33<\/td>\r\n41,45<\/td>\r\n2208,85<\/td>\r\n72,49<\/td>\r\n<\/tr>\r\n
SW-36-7-28-W1<\/td>\r\n30,91<\/td>\r\n169,49<\/td>\r\n14,42<\/td>\r\n1146,91<\/td>\r\n46,24<\/td>\r\n<\/tr>\r\n
W-34-5-27-W1<\/td>\r\n38,77<\/td>\r\n151,58<\/td>\r\n24,71<\/td>\r\n1476,83<\/td>\r\n59,82<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>"}]},"aioseo_notices":[],"lang":"en","translations":{"en":70645,"es":70686,"pt":70689,"ru":70691,"uk":70693,"fr":87085},"pll_sync_post":[],"_links":{"self":[{"href":"https:\/\/eos.com\/wp-json\/wp\/v2\/pages\/70645","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/eos.com\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/eos.com\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/eos.com\/wp-json\/wp\/v2\/users\/43"}],"replies":[{"embeddable":true,"href":"https:\/\/eos.com\/wp-json\/wp\/v2\/comments?post=70645"}],"version-history":[{"count":0,"href":"https:\/\/eos.com\/wp-json\/wp\/v2\/pages\/70645\/revisions"}],"up":[{"embeddable":true,"href":"https:\/\/eos.com\/wp-json\/wp\/v2\/pages\/44505"}],"wp:attachment":[{"href":"https:\/\/eos.com\/wp-json\/wp\/v2\/media?parent=70645"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}