TY - JOUR
T1 - Developing an operational framework to diagnose yield gaps in commercial sugarcane mills
AU - Gasparotto, Leticia G.
AU - Rosa, Juliano M.
AU - Grassini, Patricio
AU - Marin, Fábio R.
N1 - Funding Information:
We acknowledge the mill for help providing data for the execution of this study. Funding sources includes the Research Foundation of the State of São Paulo ( FAPESP grants 2017/20925-0, 2018/06396-7, 2021/00720-0 ) and The Brazilian Research Council ( CNPq 130972/2019-3, 425174/2018-2 and 300916/2018-3 ). Other funding sources include the Global Engagement Office at the Institute of Agriculture and Natural Resources at UNL, the FAPESP-UNL SPRINT Program ( 2017/50445-0 ), and the International Plant Nutrition Institute ( IPNI ).
Funding Information:
We acknowledge the mill for help providing data for the execution of this study. Funding sources includes the Research Foundation of the State of S?o Paulo (FAPESP grants 2017/20925-0, 2018/06396-7, 2021/00720-0) and The Brazilian Research Council (CNPq 130972/2019-3, 425174/2018-2 and 300916/2018-3). Other funding sources include the Global Engagement Office at the Institute of Agriculture and Natural Resources at UNL, the FAPESP-UNL SPRINT Program (2017/50445-0), and the International Plant Nutrition Institute (IPNI).
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/3/15
Y1 - 2022/3/15
N2 - Yield-gap analysis provides farmers and crop consultants with key information to identify low-performance fields and tune agronomic practices to increase yield. The objective of this study was to develop an operational framework that allows quantification of yield gaps in individual sugarcane commercial fields (referred to as ‘blocks’) and identification of their causes. We provide proof of concept about application of our framework using a sugarcane mill in southern Brazil that manages ca. 8000 blocks per year as a case study. Water-limited yield potential (Yw) was estimated for each block using a crop model coupled with local weather, soil, and crop management data, and the yield gap was estimated as the difference between Yw and actual yield. To further illustrate application of the framework, causes for yield gaps for nine individual blocks were determined based on field measurements in combination with yield-loss models derived from the literature. Average yield gap was 34 Mg ha−1, which represented 33% of simulated Yw, indicating room for increasing sugarcane yields. Across the nine blocks where causes of yield gaps were assessed, nutrient limitations, insect damage, and sprouting failures accounted for 70% of the current yield gap. In contrast, weeds and diseases were less important. Causes for yield gaps can be categorized into those associated long-term management, such as inappropriate harvest practices leading to sprouting failures and soil compaction, and those related with short-term management such as nutrient application and control of insects, pests, and diseases. The study provides an operational framework that allows sugarcane mills to diagnose yield gaps across their blocks and identify opportunities to increase yield via better agronomic management.
AB - Yield-gap analysis provides farmers and crop consultants with key information to identify low-performance fields and tune agronomic practices to increase yield. The objective of this study was to develop an operational framework that allows quantification of yield gaps in individual sugarcane commercial fields (referred to as ‘blocks’) and identification of their causes. We provide proof of concept about application of our framework using a sugarcane mill in southern Brazil that manages ca. 8000 blocks per year as a case study. Water-limited yield potential (Yw) was estimated for each block using a crop model coupled with local weather, soil, and crop management data, and the yield gap was estimated as the difference between Yw and actual yield. To further illustrate application of the framework, causes for yield gaps for nine individual blocks were determined based on field measurements in combination with yield-loss models derived from the literature. Average yield gap was 34 Mg ha−1, which represented 33% of simulated Yw, indicating room for increasing sugarcane yields. Across the nine blocks where causes of yield gaps were assessed, nutrient limitations, insect damage, and sprouting failures accounted for 70% of the current yield gap. In contrast, weeds and diseases were less important. Causes for yield gaps can be categorized into those associated long-term management, such as inappropriate harvest practices leading to sprouting failures and soil compaction, and those related with short-term management such as nutrient application and control of insects, pests, and diseases. The study provides an operational framework that allows sugarcane mills to diagnose yield gaps across their blocks and identify opportunities to increase yield via better agronomic management.
KW - Crop model
KW - Management
KW - Mill
KW - Sugarcane
KW - Yield gap
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U2 - 10.1016/j.fcr.2022.108433
DO - 10.1016/j.fcr.2022.108433
M3 - Article
AN - SCOPUS:85122671976
SN - 0378-4290
VL - 278
JO - Field Crops Research
JF - Field Crops Research
M1 - 108433
ER -