Remember your last physical checkup? The nurse took a blood sample and off it dashed to the lab. What happened next is oblivious to many of us. In fact, we only worry about the results. Complete blood counts. Cholesterol. Signs of anemia. Hints of diabetes. WE WANT STRONG HEALTH. Well today, we’re going to show you what happens in-between. Just a quick glance. With a twist. Real-world, organic style. Just imagine… your body is a smallholder agricultural landscape of West Africa. Inside you have vessels (cart paths), lymph nodes (fallows), neurons (humans), energy reserves (fields), and… blood cells (trees). Your ability to maintain healthy tree counts on your fields may well determine your future. By sustaining soil nutrients and ensuring fertility. By way of natural cooling to regulate your skin and body temperature. By raising the water table to replenish epidermal moisture. By providing nutritive complements to your diet.
SIBWA’s In Silico phase adapted the very same processing technique employed in medical imaging for blood counting to the enumeration of trees across West African agricultural landscapes. So let the laboratory results speak for themselves:
| Site | SER1 | TEG1 | NOB1 | SUK1 | FAN1 | PIS1 |
| Ecological gradient | drier | moister | ||||
| Human density pressure | high | medium | med-high | medium | low | low |
| Estimated space saturation
(% cropland) |
61.55 | 25.20 | 20.35 | 30.28 | 31.76 | 22.70 |
| Smallholder fields counts | 3,765 | 819 | 1,825 | 1,548 | 823 | 2,481 |
| Field size, ha | 1.1 ± 1.0 | 1.9 ± 1.4 | 0.7 ± 0.8 | 1.4 ± 1.2 | 2.7 ± 4.1 | 0.6 ± 0.6 |
| Trees per field | 3.2 ± 5.6 | 14.4 ± 14.1 | 7.9 ± 13.7 | 13.8 ± 16.9 | 28.5 ± 42.0 | 8.8 ± 17.0 |
| Trees per hectare | 3.4 ± 5.9 | 7.3 ± 4.2 | 8.8 ± 8.2 | 9.5 ± 7.3 | 10.8 ± 11.8 | 13.6 ± 12.3 |
| Total in-field tree counts | 12,254 | 11,566 | 14,160 | 21,964 | 23,964 | 20,510 |
Now, if estimating tree densities yields similar performance levels regardless of whether you measure it on the ground, you digitize it manually on-screen, or run an automated object extraction procedure, what direction will you choose?
| Tree position extraction time (sec) | Omission Error (%) | Commission Error (%) | |
| Ground manual (with GPS) | 120 | 0 | 0 |
| On-screen manual (operator) | 5 | 1 | 1 |
| Automatic (2.2 Ghz processor) | 0.03 | 11 | 2 |
You’re right: the fastest. Because there is more to this. Maintaining and enhancing the natural regeneration of shrubs and trees in farmer fields has numerous benefits for the resource base and for animal and human nutrition. Granted. We all know that (well, hopefully we do). But, monitoring this process efficiently has the potential to effectively unlock new opportunities for smallholders. Like, ecosystem services.
CARBON MARKETS. Incentives that could trigger a virtuous cycle. An intensification cycle. So let’s cut this loose. Let’s shortcut the dreaded “research-development continuum” hydra and its multiple aneurysms. Just bump in a SATELLITE ALLOMETRY bypass. With community-friendly Monitoring, Reporting and Verification (MRV) systems on top of that. Turn-key. Shoot the whole thing back to the voluntary carbon markets. And multiply the profits. NOW.
1. FAN= Fansirakoro, Mali; NOB= Nobere, B. Faso; PIS= Pisii, Ghana; SER= Serkin Hawsa, Niger; SUK= Sukumba, Mali; TEG= Tegena, Mali
Download PDF version: 200910XX_SIBWA_InSilico_growingstrongroots_PCST_V05
what a fantastic post, wow.