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Phosphorus (P) availability in soils limits crop yields in many regions of the World, while excess of soil P triggers aquatic eutrophication in other regions. Numerous processes drive the global spatial distribution of P in agricultural soils, but their relative roles remain unclear. Here, we combined several global data sets describing these drivers with a soil P dynamics model to simulate the distribution of P in agricultural soils and to assess the contributions of the different drivers at the global scale. We analysed both the labile inorganic P (PILAB ), a proxy of the pool involved in plant nutrition and the total soil P (PTOT ). We found that the soil biogeochemical background corresponding to P inherited from natural soils at the conversion to agriculture (BIOG) and farming practices (FARM) were the main drivers of the spatial variability in cropland soil P content but that their contribution varied between PTOT vs. PILAB . When the spatial variability was computed between grid cells at half-degree resolution, we found that almost all of the PTOT spatial variability could be explained by BIOG, while BIOG and FARM explained 38% and 63% of PILAB spatial variability, respectively. Our work also showed that the driver contribution was sensitive to the spatial scale characterizing the variability (grid cell vs. continent) and to the region of interest (global vs. tropics for instance). In particular, the heterogeneity of farming practices between continents was large enough to make FARM contribute to the variability in PTOT at that scale. We thus demonstrated how the different drivers were combined to explain the global distribution of agricultural soil P. Our study is also a promising approach to investigate the potential effect of P as a limiting factor for agroecosystems at the global scale.
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Agricultura , Fósforo/química , Solo/química , Produtos Agrícolas , PlantasRESUMO
The authors would like to correct Figure 13 and Table A2, as well as the text related to the data presented in both of them, as indicated below, considering that an error in the calculations involving Equation (2), described in the Section 2.8 of the Materials and Methods Section, resulted in the communication of incorrect values [...].
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Vegetation properties can be estimated using optical sensors, acquiring data on board of different platforms. For instance, ground-based and Unmanned Aerial Vehicle (UAV)-borne spectrometers can measure reflectance in narrow spectral bands, while different modelling approaches, like regressions fitted to vegetation indices, can relate spectra with crop traits. Although monitoring frameworks using multiple sensors can be more flexible, they may result in higher inaccuracy due to differences related to the sensors characteristics, which can affect information sampling. Also organic production systems can benefit from continuous monitoring focusing on crop management and stress detection, but few studies have evaluated applications with this objective. In this study, ground-based and UAV spectrometers were compared in the context of organic potato cultivation. Relatively accurate estimates were obtained for leaf chlorophyll (RMSE = 6.07 µg·cm-2), leaf area index (RMSE = 0.67 m²·m-2), canopy chlorophyll (RMSE = 0.24 g·m-2) and ground cover (RMSE = 5.5%) using five UAV-based data acquisitions, from 43 to 99 days after planting. These retrievals are slightly better than those derived from ground-based measurements (RMSE = 7.25 µg·cm-2, 0.85 m²·m-2, 0.28 g·m-2 and 6.8%, respectively), for the same period. Excluding observations corresponding to the first acquisition increased retrieval accuracy and made outputs more comparable between sensors, due to relatively low vegetation cover on this date. Intercomparison of vegetation indices indicated that indices based on the contrast between spectral bands in the visible and near-infrared, like OSAVI, MCARI2 and CIg provided, at certain extent, robust outputs that could be transferred between sensors. Information sampling at plot level by both sensing solutions resulted in comparable discriminative potential concerning advanced stages of late blight incidence. These results indicate that optical sensors, and their integration, have great potential for monitoring this specific organic cropping system.
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Solanum tuberosum , Clorofila , Folhas de PlantaRESUMO
Root herbivores pose a major threat to agricultural crops. They are difficult to control and their damage often goes unnoticed until the larvae reach their most devastating late instar stages. Crop diversification can reduce pest pressure, generally without compromising yield. We studied how different diversified cropping systems affected the oviposition and abundance of the specialist cabbage root fly Delia radicum, the most important root herbivore in Brassica crops. The cropping systems included a monoculture, pixel cropping, and four variations of strip cropping with varying intra- and interspecific crop diversity, fertilization and spatial configuration. Furthermore, we assessed whether there was a link between D. radicum and other macroinvertebrates associated with the same plants. Cabbage root fly oviposition was higher in strip cropping designs compared to the monoculture and was highest in the most diversified strip cropping design. Despite the large number of eggs, there were no consistent differences in the number of larvae and pupae between the cropping systems, indicative of high mortality of D. radicum eggs and early instars especially in the strip cropping designs. D. radicum larval and pupal abundance positively correlated with soil-dwelling predators and detritivores and negatively correlated with other belowground herbivores. We found no correlations between the presence of aboveground insect herbivores and the number of D. radicum on the roots. Our findings indicate that root herbivore presence is determined by a complex interplay of many factors, spatial configuration of host plants, and other organisms residing near the roots. Supplementary Information: The online version contains supplementary material available at 10.1007/s10340-023-01629-1.
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Intercropping is both a well-established and yet novel agricultural practice, depending on one's perspective. Such perspectives are principally governed by geographic location and whether monocultural practices predominate. Given the negative environmental effects of monoculture agriculture (loss of biodiversity, reliance on non-renewable inputs, soil degradation, etc.), there has been a renewed interest in cropping systems that can reduce the impact of modern agriculture while maintaining (or even increasing) yields. Intercropping is one of the most promising practices in this regard, yet faces a multitude of challenges if it is to compete with and ultimately replace the prevailing monocultural norm. These challenges include the necessity for more complex agricultural designs in space and time, bespoke machinery, and adapted crop cultivars. Plant breeding for monocultures has focused on maximizing yield in single-species stands, leading to highly productive yet specialized genotypes. However, indications suggest that these genotypes are not the best adapted to intercropping systems. Re-designing breeding programs to accommodate inter-specific interactions and compatibilities, with potentially multiple different intercropping partners, is certainly challenging, but recent technological advances offer novel solutions. We identify a number of such technology-driven directions, either ideotype-driven (i.e., "trait-based" breeding) or quantitative genetics-driven (i.e., "product-based" breeding). For ideotype breeding, plant growth modeling can help predict plant traits that affect both inter- and intraspecific interactions and their influence on crop performance. Quantitative breeding approaches, on the other hand, estimate breeding values of component crops without necessarily understanding the underlying mechanisms. We argue that a combined approach, for example, integrating plant growth modeling with genomic-assisted selection and indirect genetic effects, may offer the best chance to bridge the gap between current monoculture breeding programs and the more integrated and diverse breeding programs of the future.