Biophysical and management factors causing yield gap in soybean in the subtropics of Brazil

Abstract

Little is known about the relationships between soybean [Glycine max (L.) Merr.] maturity grouping and the yield-limiting factors in the subtropics. This information can be used to estimate soybean potential yield (Yp) and the water-limited yield (Yw) and to optimize current soybean management practices to improve yield and resource use efficiency. The objectives were (a) to estimate the Yp, Yw, and yield gaps (YGs) of soybean in subtropical Brazil and (b) to identify the biophysical and management factors which potentially explain the YG. The CSM CROPGRO model that was calibrated with data collected between 2011 and 2019 was used to estimate the influence of sowing date and maturity group (MG) on yield potentials and water efficiencies. Yield varied from 6.1 to 7.2 Mg ha −1 and from 2.5 to 5.1 Mg ha −1 between buffer zones (BZs) for Yp and Yw, respectively. The YG caused by water deficit (YGw) ranged from 26% (1.8 Mg ha −1 ) to 62% (4.1 Mg ha −1 ) of the Yp and the YG caused by management (e.g., sowing date, MG, final density) ranged from 9% (0.6 Mg ha −1 ) to 39% (2.7 Mg ha −1 ) of the Yp. The main management factor of the YG was sowing date. The potential yield was higher in the early MGs, showing greater water use efficiency in MG ≤ 5.5 (9.6 kg ha −1 mm −1 ) than in high MGs (MG ≥ 5.6). Findings from this study can be used by agronomists in subtropical regions to optimize soybean yields.

Growth and transpiration of soybean genotypes with HaHB4® transcription factor for drought tolerance

“…In this study, we compare the performance of commercial cultivars used in Brazil with HaHB4® genotypes under conditions of induced water deficit during the vegetative phase. A small increase in drought tolerance in new soybean cultivars compared to commonly sown cultivars can significantly increase Brazilian production, where water deficiency is the main productivity limiting factor (Sentelhas et al, 2015; Tagliapietra et al, 2021; Global Yield Gap Atlas, 2021).…”

Biological nitrogen fixation and prospects for ecological intensification in cereal-based cropping systems

“…Given that close relationships have frequently been observed between legume productivity and the amounts of N 2 fixed by many different crop and forage legumes growing across a diverse range of environments and geographic regions of the world (e.g., West and South Asia – Pilbeam et al, 1997 ; Maskey et al, 2001 ; North and South America – Walley et al, 2007 ; Espinoza et al, 2012 ; Africa – Vanlauwe et al, 2019 ; Oceania – Unkovich et al, 2010 ; Peoples et al, 2012 ; Europe – Carlsson and Huss-Danell, 2003 ; Anglade et al, 2015 ), management options specifically aimed at supporting greater legume growth will generally have the desired effect of improving inputs of fixed N. The identification of those countries, regions, localities or farming systems with the greatest potential for improvements in legume productivity can be assisted through the judicious use of “yield-gap” analyses, which compare current farmer yields to either experimental or breeders’ plot yields in the same environment, or simulated predictions of “water-limited yield potential” based on climatic records and soil water-holding capacity and nutritional characteristics ( Bhatia et al, 2006 , Grassini et al, 2015 , Van Loon et al, 2018 , Tagliapietra et al, 2021 ). The underlying causes of yield gaps could be further explored using either simulation models and/or meta-analyses of large datasets containing producer field-level yield and management records ( Grassini et al, 2015 ; Hochman et al, 2020 ; Mourtzinis et al, 2020 ; Tagliapietra et al, 2021 ). Often all that might be needed to make progress towards overcoming constraints to productivity revealed by yield-gap analyses is to assist farmers to implement their existing knowledge and adopt known best-management practices ( Giller and Cadisch, 1995 , Crews and Peoples, 2004 ).…”

“…Often all that might be needed to make progress towards overcoming constraints to productivity revealed by yield-gap analyses is to assist farmers to implement their existing knowledge and adopt known best-management practices ( Giller and Cadisch, 1995 , Crews and Peoples, 2004 ). However, in general terms, imposed management strategies would need to consider time of sowing in relation to soil water availability and seasonal water supply, and the length of the effective growing season, as well as avoiding sensitive periods of growth and flowering when there is an elevated probability of frost, drought or high temperatures ( Beck et al, 1991 , Peoples et al, 2009a , Santachiara et al, 2019 , Tagliapietra et al, 2021 ). Unfavorable and hostile soils which either limit legume root exploration (e.g., soil compaction, sodicity, salinity), inhibit nodulation, or restrict shoot growth (e.g., soil acidity, nutrient deficiencies) should also be ameliorated ( Giller and Cadisch, 1995 , Peoples et al, 2009a , Santachiara et al, 2019 , Vanlauwe et al, 2019 , Baijukya et al, 2021 ).…”

“…Attention would also need to be given to reducing the incidence of pests, diseases and weed competition responsible for lowering productivity ( Beck et al, 1991 , Peoples et al, 1995a , Singh et al, 2009 ; Table 13 ). In terms of genetic factors, the choice of legume species (and maturity group) most adapted for the local soil type, season or climate is likely to play a crucial role ( Peoples et al, 2009a , Tagliapietra et al, 2021 ), as will plant improvement for enhanced disease resistance ( Giller and Cadisch, 1995 , Peoples et al, 2019 ). In the case of forage systems, enhancing legume biomass may require improvements in the proportion, persistence and growth of the legume component of pasture swards through the choice of species mix sown, sowing practices, amelioration of soil acidity, provision of additional phosphorus supply, and management of timing and frequency of grazing or cutting ( Ledgard, 2001 , Peoples and Baldock, 2001 , Carlsson and Huss-Danell, 2003 , Rochon et al, 2004 , Peoples et al, 2012 ).…”

“…Simulations models provide the ability to undertake life-cycle analyses of fossil energy consumption, or to design, predict and compare long-term requirements for inputs of N fertilizer and the implicit environmental costs (NO 3 leaching, greenhouse gas emission outcomes) of different alternative cropping sequences ( Costa et al, 2021 , Hochman et al, 2021 ). This would enable the identification of where the N benefits are likely to be the greatest, and provide information on cropping systems and sequences most suited for different agroecological zones ( Smith and Chalk, 2020 , Tagliapietra et al, 2021 ). Such approaches can also be utilized as valuable educational tools, to arm producers with the knowledge and information about the likely yield and economic outcomes of different cropping sequence senarios, needed to aid decision-making regarding whether or not to include more legumes in their farming system ( Pelzer et al, 2020 ).…”