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1<div class="post-body mt-10"><span id="docs-internal-guid-5f4623af-7fff-e55f-b58e-eff8fd3eaebb"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">For U.S. row-crop operators building a digital-first farm, two practical AI-enabled applications stand out: variable-rate nitrogen (VRN) and computer-vision-based see-and-spray weed control. Both use field-level data to make input decisions more precise, while allowing farmers to measure the economic impact through fertilizer and herbicide use.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">The economics vary significantly by crop, field conditions, input prices, weed pressure, technology costs and the quality of the underlying data. That makes these technologies less about a universal payback number and more about identifying fields where precision application can create measurable value.</span></p><h2 dir="ltr" style="line-height:1.38;margin-top:18pt;margin-bottom:6pt;"><span style="font-size: 16pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">Why these two use cases matter</span></h2><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">VRN and targeted spraying address two major input categories: nitrogen fertilizer and herbicides. They also build on data and equipment increasingly common across U.S. farms, including yield maps, crop imagery, sensors, section control and farm-management platforms.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">That makes them practical entry points for digital agriculture. Instead of starting with a complex AI pilot, operators can apply data-driven decision-making to an existing production workflow and measure changes in input use, yield and field-level profitability.</span></p><h2 dir="ltr" style="line-height:1.38;margin-top:18pt;margin-bottom:6pt;"><span style="font-size: 16pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">Variable-rate nitrogen: what the evidence shows</span></h2><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">Midwest research from the early 2020s highlights both the opportunity and the variability of VRN. A study covering 17 field-years across 13 Midwest fields from 2021 to 2023 found that the profitability of prescriptions based on remote sensing compared with yield-history-based prescriptions ranged from −$410 to +$350 per hectare. The results varied by season: NDVI-based prescriptions performed better when early-season crop conditions persisted, while yield-history prescriptions performed better when early-season conditions did not persist.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">That finding is important because it shows why VRN should not be treated as an automatic saving. The value comes from matching the nitrogen recommendation to actual field and seasonal conditions.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">A separate </span><a href="https://projects.sare.org/project-reports/fnc17-1100/" style="text-decoration:none;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(17, 85, 204); background-color: transparent; font-variant: normal; text-decoration: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">Nebraska SARE project </span></a><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">evaluated crop-canopy sensing and variable-rate, in-season nitrogen application. The project found that sensor-guided in-season nitrogen management can reduce nitrogen application while maintaining production and improving economic performance, although results differed between years and sites.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">There is also a more immediate opportunity for growers to revisit nitrogen rates even without deploying a new VRN system. A March 2026 analysis from the University of Illinois foun
1d that higher nitrogen prices reduced the economically optimal MRTN rate for central Illinois corn. Under its spring 2026 pricing scenario, reducing the recommended rate by 8 lb nitrogen per acre could save about $4.88 per acre with anhydrous ammonia or just over $7 per acre with liquid nitrogen or urea, depending on the product used.</span></p><h2 dir="ltr" style="line-height:1.38;margin-top:18pt;margin-bottom:6pt;"><span style="font-size: 16pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">How VRN creates value</span></h2><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">VRN translates differences in soil, historical yield, crop vigor and growing-season conditions into more targeted nitrogen recommendations. The goal is not simply to apply less nitrogen. It is to improve the relationship between nitrogen availability, crop demand and the economics of each field.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">The SARE research demonstrates the potential of crop sensors to direct in-season N applications, while newer Midwest research shows that combining yield history with current-season crop-vigor information can improve prescription decisions.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">That distinction matters. A digital-first farm should evaluate VRN by measuring nitrogen applied, yield response, nitrogen-use efficiency and net return rather than relying on a fixed percentage-saving assumption.</span></p><h2 dir="ltr" style="line-height:1.38;margin-top:18pt;margin-bottom:6pt;"><span style="font-size: 16pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">See-and-spray: measurable savings from targeted application</span></h2><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">Computer-vision spraying provides another relatively direct way to connect AI with input economics. Systems such as John Deere See &amp; Spray use cameras and onboard processing to identify weeds and activate individual spray nozzles, allowing herbicide to be applied only where weeds are detected.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">John Deere reported that its See &amp; Spray technology was used across more than 5 million acres during the 2025 growing season. Customers reduced non-residual herbicide use by an average of nearly 50%, saving nearly 31 million gallons of herbicide mix. These figures are company-reported commercial deployment results rather than a controlled independent trial, but they demonstrate that targeted spraying has reached substantial operating scale.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">Independent research also shows that targeted spraying can produce substantial reductions in herbicide use. A 2026 Association of Equipment Manufacturers review cited research showing reductions ranging from roughly 40% to 60% in targeted-spray applications, while individual studies have reported wider ranges depending on weed distribution, crop and operating conditions.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">
1The economic benefit is therefore highly dependent on the field. Patchy weed pressure creates more opportunity for targeted application because a large proportion of the field may not require a full-rate broadcast treatment.</span></p><h2 dir="ltr" style="line-height:1.38;margin-top:18pt;margin-bottom:6pt;"><span style="font-size: 16pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">When see-and-spray has the strongest economic case</span></h2><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">The technology is particularly relevant where weeds are spatially variable and post-emergence herbicide applications represent a significant portion of crop-protection costs.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">Farmers evaluating the technology should compare broadcast herbicide use with targeted application on a field-by-field basis. Important measurements include gallons or pounds of active ingredient applied, acres actually sprayed, technology and licensing costs, weed-control performance, crop response and total herbicide expenditure.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">This approach produces a more reliable farm-specific ROI than applying a single industry-wide savings percentage.</span></p><h2 dir="ltr" style="line-height:1.38;margin-top:18pt;margin-bottom:6pt;"><span style="font-size: 16pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">From AI pilots to measurable farm economics</span></h2><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">For U.S. agribusinesses and AgTech providers, the lesson is not that every farm will achieve the same ROI from VRN or see-and-spray. It is that these technologies offer relatively clear pathways for connecting AI with measurable production decisions.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">The strongest business case comes when technology is evaluated against real field conditions, input prices and farm-level economics. VRN can use historical and in-season information to refine nitrogen decisions, while computer vision can make herbicide application more targeted. Both also generate operational data that can support more advanced digital agriculture strategies.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">For growers, lenders, investors and technology providers, that combination of measurable inputs, field-level data and repeatable decision-making is an important foundation for scaling AI in U.S. agriculture.</span></p><h2 dir="ltr" style="line-height:1.38;margin-top:18pt;margin-bottom:6pt;"><span style="font-size: 16pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">AgriNext Awards &amp; Conference USA 2027</span></h2><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">The conversation around practical AI adoption in agriculture will continue at AgriNext Awards &amp; Conference USA 2027, taking place on 9 April 2027 at JW Marriott Las Vegas Resort &amp; Spa, Las Vegas, USA. The event brings together growers, agribusiness leaders, AgTech innovators, investors, researchers and policymakers to explore AI, precision farming, sustainability and other technologies shaping the future of agriculture.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">Learn more: </span><a href="http://us.agrinextcon.com/" style="text-decoration:none;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(17, 85, 204); background-color: transparent; font-variant: normal; text-decoration: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">agrinextcon.com</span></a></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">References</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">
1University of Illinois FarmDoc —</span><a href="https://farmdocdaily.illinois.edu/2026/03/high-fertilizer-prices-suggest-reconsidering-application-rates.html" style="text-decoration:none;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(17, 85, 204); background-color: transparent; font-variant: normal; text-decoration: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;"> High Fertilizer Prices Suggest Reconsidering Application Rates</span></a></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;">SARE — </span><a href="https://projects.sare.org/sare_project/fnc17-1100/" style="text-decoration:none;"><span style="font-size: 11pt; font-family: Arial, sans-serif; color: rgb(17, 85, 204); background-color: transparent; font-variant: normal; text-decoration: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">New technologies for improving sustainability of corn Nitrogen management</span></a></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt;"><br></p></span></div></article><aside class="grid h-fit gap-6 lg:sticky lg:top-28"><section class="card-elevated p-7"><h2 class="font-display text-lg font-extrabold uppercase tracking-tight">About the author</h2><p class="mt-4 text-sm text-muted-foreground">Next Business Media produces the AgriNext Awards &amp; Conference series and reports on how enterprises put artificial intelligence to work across the Middle East, Asia and the US.</p><a href="/blog" class="mt-5 inline-block text-sm font-semibold text-primary hover:underline">More from this author →</a></section><section class="card-elevated p-7"><h2 class="font-display text-lg font-extrabold uppercase tracking-tight">Most recent</h2><ul class="mt-5 grid gap-5"><li><a href="/climate-smart-agriculture-building-resilient-food-systems" class="group flex gap-3"><img src="/api/public/media/1789976443248-5dmy3n.jpeg" alt="" loading="lazy" class="size-16 shrink-0 rounded-xl object-cover"/><span><span class="line-clamp-2 text-sm font-semibold leading-snug group-hover:text-primary">Climate-Smart Agriculture: Building Resilient Food Systems</span><span class="mt-1 block text-xs text-muted-foreground">21 September 2026</span></span></a></li><li><a href="/from-data-to-dollars-turning-african-farm-data-into-bankable-assets" class="group flex gap-3"><img src="/api/public/media/1789889050540-c4axyq.jpeg" alt="" loading="lazy" class="size-16 shrink-0 rounded-xl object-cover"/><span><span class="line-clamp-2 text-sm font-semibold leading-snug group-hover:text-primary">From Data to Dollars: Turning African Farm Data into Bankable Assets</span><span class="mt-1 block text-xs text-muted-foreground">20 September 2026</span></span></a></li><li><a href="/transforming-irrigation-through-atmospheric-water-optimization" class="group flex gap-3"><img src="/api/public/media/1789621773846-oejw4b.jpeg" alt="" loading="lazy" class="size-16 shrink-0 rounded-xl object-cover"/><span><span class="line-clamp-2 text-sm font-semibold leading-snug group-hover:text-primary">Transforming Irrigation Through Atmospheric Water Optimization</span><span class="mt-1 block text-xs text-muted-foreground">17 September 2026</span></span></a></li><li><a href="/from-desert-to-harvest-how-water-smart-agriculture-is-securing-dubai-s-food-future" class="group flex gap-3"><img src="/api/public/media/1789359153550-vs8m3y.jpeg" alt="" loading="lazy" class="size-16 shrink-0 rounded-xl object-cover"/><span><span class="line-clamp-2 text-sm font-semibold leading-snug group-hover:text-primary">From Desert to Harvest: How Water-Smart Agriculture Is Securing Dubai’s Food Future</span><span class="mt-1 block text-xs text-muted-foreground">14 September 2026</span></span></a></li></ul></section><section class="card-elevated bg-ink p-7 text-ink-foreground"><h2 class="font-display text-lg font-extrabold uppercase tracking-tight">Stay updated</h2>
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1<script class="$tsr" id="$tsr-stream-barrier">(self.$R=self.$R||{})["tsr"]=[];self.$_TSR={h(){this.hydrated=!0,this.c()},e(){this.streamEnded=!0,this.c()},c(){this.hydrated&&this.streamEnded&&(delete self.$_TSR,delete self.$R.tsr)},p(e){this.initialized?e():this.buffer.push(e)},buffer:[]};$_TSR.router=($R=>$R[0]={manifest:$R[1]={routes:$R[2]={__root__:$R[3]={preloads:$R[4]=["/assets/index-DBd5aPPm.js","/assets/jsx-runtime-DGeXAQPT.js","/assets/react-BhjfaixL.js","/assets/react-dom-D15Ble1V.js","/assets/portal.functions-CgfHhe_r.js","/assets/useStore-BQsAo5OC.js","/assets/invariant-DEEwAagU.js","/assets/redirect-Coy-z-9D.js"],scripts:$R[5]=[$R[6]={attrs:$R[7]={type:"module",async:!0,src:"/assets/index-DBd5aPPm.js"}}]},"/$slug":$R[8]={preloads:$R[9]=["/assets/_slug-A_PvX72u.js","/assets/site-footer-V4iepM0O.js","/assets/_slug-DM2Gaqyo.js","/assets/_slug-uObwoZSn.js"]}}},matches:$R[10]=[$R[11]={i:"__root__�",u:1790316474081,s:"success",l:$R[12]={favicon:"/api/public/media/1786417315483-yxlipk.png",gdpr:$R[13]={enabled:!0,message:"We use cookies to run this site, understand how it is used and improve your experience. You can accept all cookies or continue with only the essential ones.",acceptLabel:"Accept all",declineLabel:"Essential only",policyLabel:"Privacy policy",policyHref:""}},ssr:!0},$R[14]={i:"�$slug�the-first-two-ai-moves-that-pay-variable-rate-nitrogen-and-see-and-spray-in-the-u-s",u:1790316474387,s:"success",l:$R[15]={slug:"the-first-two-ai-moves-that-pay-variable-rate-nitrogen-and-see-and-spray-in-the-u-s",title:"The First Two AI Moves That Pay: Variable-Rate Nitrogen and See‑and‑Spray in the U.S.",excerpt:"Variable-rate nitrogen and computer-vision spraying are two practical AI applications for U.S. row-crop farms, helping turn field data into more efficient fertilizer and herbicide use while supporting farm-level profitability.\n\n",featured_image:"/api/public/media/1790240265032-gwfh78.jpeg",published_at:"2026-09-24T09:21:51.215+00:00",category:"Artificial Intelligence in Agriculture",author:"",tags:$R[16]=["Variable-rate nitrogen","See-and-spray","Precision agriculture","AI in farming","Input efficiency","Digital agriculture","U.S. row crops","AgriNext 2027"],content_html:"\x3Cspan id=\"docs-internal-guid-5f4623af-7fff-e55f-b58e-eff8fd3eaebb\">\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">For U.S. row-crop operators building a digital-first farm, two practical AI-enabled applications stand out: variable-rate nitrogen (VRN) and computer-vision-based see-and-spray weed control. Both use field-level data to make input decisions more precise, while allowing farmers to measure the economic impact through fertilizer and herbicide use.\x3C/span>\x3C/p>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">The economics vary significantly by crop, field conditions, input prices, weed pressure, technology costs and the quality of the underlying data. That makes these technologies less about a universal payback number and more about identifying fields where precision application can create measurable value.\x3C/span>\x3C/p>\x3Ch2 dir=\"ltr\" style=\"line-height:1.38;margin-top:18pt;margin-bottom:6pt;\">\x3Cspan style=\"font-size: 16pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">Why these two use cases matter\x3C/span>\x3C/h2>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">VRN and targeted spraying address two major input categories: nitrogen fertilizer and herbicides. They also build on data and equipment increasingly common across U.S. farms, including yield maps, crop imagery, sensors, section control and farm-management platforms.\x3C/sp
1an>\x3C/p>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">That makes them practical entry points for digital agriculture. Instead of starting with a complex AI pilot, operators can apply data-driven decision-making to an existing production workflow and measure changes in input use, yield and field-level profitability.\x3C/span>\x3C/p>\x3Ch2 dir=\"ltr\" style=\"line-height:1.38;margin-top:18pt;margin-bottom:6pt;\">\x3Cspan style=\"font-size: 16pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">Variable-rate nitrogen: what the evidence shows\x3C/span>\x3C/h2>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">Midwest research from the early 2020s highlights both the opportunity and the variability of VRN. A study covering 17 field-years across 13 Midwest fields from 2021 to 2023 found that the profitability of prescriptions based on remote sensing compared with yield-history-based prescriptions ranged from −$410 to +$350 per hectare. The results varied by season: NDVI-based prescriptions performed better when early-season crop conditions persisted, while yield-history prescriptions performed better when early-season conditions did not persist.\x3C/span>\x3C/p>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">That finding is important because it shows why VRN should not be treated as an automatic saving. The value comes from matching the nitrogen recommendation to actual field and seasonal conditions.\x3C/span>\x3C/p>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">A separate \x3C/span>\x3Ca href=\"https://projects.sare.org/project-reports/fnc17-1100/\" style=\"text-decoration:none;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(17, 85, 204); background-color: transparent; font-variant: normal; text-decoration: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;\">Nebraska SARE project \x3C/span>\x3C/a>\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">evaluated crop-canopy sensing and variable-rate, in-season nitrogen application. The project found that sensor-guided in-season nitrogen management can reduce nitrogen application while maintaining production and improving economic performance, although results differed between years and sites.\x3C/span>\x3C/p>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">There is also a more immediate opportunity for growers to revisit nitrogen rates even without deploying a new VRN system. A March 2026 analysis from the University of Illinois found that higher nitrogen prices reduced the economically optimal MRTN rate for central Illinois corn. Under its spring 2026 pricing scenario, reducing the recommended rate by 8 lb nitrogen per acre could save about $4.88 per acre with anhydrous ammonia or just over $7 per acre with liquid nitrogen or urea, depending on the product used.\x3C/sp
1an>\x3C/p>\x3Ch2 dir=\"ltr\" style=\"line-height:1.38;margin-top:18pt;margin-bottom:6pt;\">\x3Cspan style=\"font-size: 16pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">How VRN creates value\x3C/span>\x3C/h2>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">VRN translates differences in soil, historical yield, crop vigor and growing-season conditions into more targeted nitrogen recommendations. The goal is not simply to apply less nitrogen. It is to improve the relationship between nitrogen availability, crop demand and the economics of each field.\x3C/span>\x3C/p>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">The SARE research demonstrates the potential of crop sensors to direct in-season N applications, while newer Midwest research shows that combining yield history with current-season crop-vigor information can improve prescription decisions.\x3C/span>\x3C/p>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">That distinction matters. A digital-first farm should evaluate VRN by measuring nitrogen applied, yield response, nitrogen-use efficiency and net return rather than relying on a fixed percentage-saving assumption.\x3C/span>\x3C/p>\x3Ch2 dir=\"ltr\" style=\"line-height:1.38;margin-top:18pt;margin-bottom:6pt;\">\x3Cspan style=\"font-size: 16pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">See-and-spray: measurable savings from targeted application\x3C/span>\x3C/h2>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">Computer-vision spraying provides another relatively direct way to connect AI with input economics. Systems such as John Deere See &amp; Spray use cameras and onboard processing to identify weeds and activate individual spray nozzles, allowing herbicide to be applied only where weeds are detected.\x3C/span>\x3C/p>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">John Deere reported that its See &amp; Spray technology was used across more than 5 million acres during the 2025 growing season. Customers reduced non-residual herbicide use by an average of nearly 50%, saving nearly 31 million gallons of herbicide mix. These figures are company-reported commercial deployment results rather than a controlled independent trial, but they demonstrate that targeted spraying has reached substantial operating scale.\x3C/span>\x3C/p>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">Independent research also shows that targeted spraying can produce substantial reductions in herbicide use. A 2026 Association of Equipment Manufacturers review cited research showing reductions ranging from roughly 40% to 60% in targeted-spray applications, while individual studies have reported wider ranges depending on weed distribution, crop and operating conditions.\x3C/sp
1an>\x3C/p>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">The economic benefit is therefore highly dependent on the field. Patchy weed pressure creates more opportunity for targeted application because a large proportion of the field may not require a full-rate broadcast treatment.\x3C/span>\x3C/p>\x3Ch2 dir=\"ltr\" style=\"line-height:1.38;margin-top:18pt;margin-bottom:6pt;\">\x3Cspan style=\"font-size: 16pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">When see-and-spray has the strongest economic case\x3C/span>\x3C/h2>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">The technology is particularly relevant where weeds are spatially variable and post-emergence herbicide applications represent a significant portion of crop-protection costs.\x3C/span>\x3C/p>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">Farmers evaluating the technology should compare broadcast herbicide use with targeted application on a field-by-field basis. Important measurements include gallons or pounds of active ingredient applied, acres actually sprayed, technology and licensing costs, weed-control performance, crop response and total herbicide expenditure.\x3C/span>\x3C/p>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">This approach produces a more reliable farm-specific ROI than applying a single industry-wide savings percentage.\x3C/span>\x3C/p>\x3Ch2 dir=\"ltr\" style=\"line-height:1.38;margin-top:18pt;margin-bottom:6pt;\">\x3Cspan style=\"font-size: 16pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">From AI pilots to measurable farm economics\x3C/span>\x3C/h2>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">For U.S. agribusinesses and AgTech providers, the lesson is not that every farm will achieve the same ROI from VRN or see-and-spray. It is that these technologies offer relatively clear pathways for connecting AI with measurable production decisions.\x3C/span>\x3C/p>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">The strongest business case comes when technology is evaluated against real field conditions, input prices and farm-level economics. VRN can use historical and in-season information to refine nitrogen decisions, while computer vision can make herbicide application more targeted. Both also generate operational data that can support more advanced digital agriculture strategies.\x3C/span>\x3C/p>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">For growers, lenders, investors and technology providers, that combination of measurable inputs, field-level data and repeatable decision-making is an important foundation for scaling AI in U.S. agriculture.\x3C/span>\x3C/p>\x3Ch2 dir=\"ltr\" style=\"line-height:1.38;margin-top:18pt;margin-bottom:6pt;\">\x3Cspan style=\"font-size: 16pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">AgriNext Awards &amp; Conference USA 2027\x3C/span>\x3C/h2>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">The conversation around practical AI adoption in agriculture will continue at AgriNext Awards &amp; Conference USA 2027, taking place on 9 April 2027 at JW Marriott Las Vegas Resort &amp; Spa, Las Vegas, USA. The event brings together growers, agribusiness leaders, AgTech innovators, investors, researchers and policymakers to explore AI, precision farming, sustainability and other technologies shaping the future of agriculture.\x3C/span>\x3C/p>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">Learn more: \x3C/span>\x3Ca href=\"http://us.agrinextcon.com/\" style=\"text-decoration:none;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(17, 85, 204); background-color: transparent; font-variant: normal; text-decoration: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;\">agrinextcon.com\x3C/span>\x3C/a>\x3C/p>\x3Cbr>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-weight: 700; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">References\x3C/span>\x3C/p>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">University of Illinois FarmDoc —\x3C/span>\x3Ca href=\"https://farmdocdaily.illinois.edu/2026/03/high-fertilizer-prices-suggest-reconsidering-application-rates.html\" style=\"text-decoration:none;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(17, 85, 204); background-color: transparent; font-variant: normal; text-decoration: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;\"> High Fertilizer Prices Suggest Reconsidering Application Rates\x3C/span>\x3C/a>\x3C/p>\x3Cbr>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(0, 0, 0); background-color: transparent; font-variant: normal; vertical-align: baseline; white-space: pre-wrap;\">
1SARE — \x3C/span>\x3Ca href=\"https://projects.sare.org/sare_project/fnc17-1100/\" style=\"text-decoration:none;\">\x3Cspan style=\"font-size: 11pt; font-family: Arial, sans-serif; color: rgb(17, 85, 204); background-color: transparent; font-variant: normal; text-decoration: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;\">New technologies for improving sustainability of corn Nitrogen management\x3C/span>\x3C/a>\x3C/p>\x3Cp dir=\"ltr\" style=\"line-height:1.38;margin-top:0pt;margin-bottom:0pt;\">\x3Cbr>\x3C/p>\x3C/span>"},ssr:!0}],lastMatchId:"�$slug�the-first-two-ai-moves-that-pay-variable-rate-nitrogen-and-see-and-spray-in-the-u-s"})($R["tsr"]);$_TSR.e();document.currentScript.remove()</script>
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