Rainfall diagnosis in context of cereal yields in Gujarat

The present study was undertaken to analyze annual average rainfall in Gujarat in order to classify and define the targeted zones and to know the impact of rainfall variability on agriculture in the state. The results revealed that the Northwest Agro-climatic zone was the most vulnerable zone among all the agro-climatic zones due to extreme deviations in rainfall pattern. This was followed by north Saurashtra, South saurashtra and Middle Gujarat Zone. The southern hills zone had the least per cent of years with extreme deviations. Water management practices such as drip irrigation, deepening wells, constructing check-dams; integrated watershed management as well as insurance coverage and microfinancing facilities have been suggested as mitigation strategies to overcome the adverse impact of rainfall variability on agricultural production.

Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco

Accurate seasonal forecasting of cereal yields is an important decision support tool for countries, such as Morocco, that are not self-sufficient in order to predict, as early as possible, importation needs. This study aims to develop an early forecasting model of cereal yields (soft wheat, barley and durum wheat) at the scale of the agricultural province considering the 15 most productive over 2000–2017 (i.e., 15 × 18 = 270 yields values). To this objective, we built on previous works that showed a tight linkage between cereal yields and various datasets including weather data (rainfall and air temperature), regional climate indices (North Atlantic Oscillation in particular), and drought indices derived from satellite observations in different wavelengths. The combination of the latter three data sets is assessed to predict cereal yields using linear (Multiple Linear Regression, MLR) and non-linear (Support Vector Machine, SVM; Random Forest, RF, and eXtreme Gradient Boost, XGBoost) machine learning algorithms. The calibration of the algorithmic parameters of the different approaches are carried out using a 5-fold cross validation technique and a leave-one-out method is implemented for model validation. The statistical metrics of the models are first analyzed as a function of the input datasets that are used, and as a function of the lead times, from 4 months to 2 months before harvest. The results show that combining data from multiple sources outperformed models based on one dataset only. In addition, the satellite drought indices are a major source of information for cereal prediction when the forecasting is carried out close to harvest (2 months before), while weather data and, to a lesser extent, climate indices, are key variables for earlier predictions. The best models can accurately predict yield in January (4 months before harvest) with an R2 = 0.88 and RMSE around 0.22 t. ha−1. The XGBoost method exhibited the best metrics. Finally, training a specific model separately for each group of provinces, instead of one global model, improved the prediction performance by reducing the RMSE by 10% to 35% depending on the provinces. In conclusion, the results of this study pointed out that combining remote sensing drought indices with climate and weather variables using a machine learning technique is a promising approach for cereal yield forecasting.

The Most Important Fungal Diseases of Cereals—Problems and Possible Solutions

The level of cereal yields and the quality of these yields depend, to a large extent, on a crop management system, the genetic potential of a given cultivar, but also on factors that may cause damage to plants or a reduction in yield. Such factors include fungal diseases of cereals, which may cause a reduction in yield by 15–20%, and in extreme cases even by 60%. The main factors determining the occurrence of these pathogens are the weather conditions during the growing season of plants, crop rotation, the previous crop, the soil tillage system, and nitrogen fertilisation. Fungal diseases of cereals limit plant growth and development, as well as reduce grain yield and quality. This paper reviews the literature on fungal diseases of cereals.

Spatial distribution of cereal yields related to the application of soil-improving cropping systems (SICS)

<p>Research indicates that spatial differentiation of crop yields and soil properties are largely influenced by agricultural practices and the nature of the soil itself. The aim of this study was to examine the spatial relationship between cereal (wheat and oats ) yields and soil properties related to the application of soil-improving cropping systems (SICS). Four-year experiment (2017-2020) was carried out on low productive sandy soil with application of following SICS: S1 – control; S2 – liming; S3 – green manure/cover crops including lupine, phacelia, serradella; S4 – manure and S5 – manure, liming and cover crops together. Effect of the SICS was evaluated using classical statistics, Bland-Altman analysis and geostatistical methods. Mathematical functions, fitted to the experimental cross- and semivariograms were used for mapping the yields (grain and straw) by ordinary cokriging. The grain yields in years with normal rainfall increased by 2% for S2, 10% for S3, 46% for S4, 47% for S5 compared to control (S1) 2789 kg/ha and in dry years were lower (respectively for S2-S5 by 16.3, 10.6, 2.8, 9.9% compared to control 1567 kg/ha. The range of spatial dependence for the yields in direct semi-variograms varied was 50–100 m and > 100 m in cross-semivariograms using textural fractions as secondary variables. The spatial relationships were stronger between yield and soil texture and properties were much stronger with texture and cation exchange capacity than with pH and organic carbon content. Using cokriging for interpolation (mapping) allowed the delineation of zones of lower and higher cereal yields including areas of the SICS application. Higher cereal yield and lower spatial variability in the areas of SICS compared to control soil were observed in the years with normal rainfall. Analysis of the Bland-Altman including limits of agreement enabled to quantify the effect of particular SICS on cereal yield vs. control reference. Different effect of particular SICS on the cereal yield was observed in the years with scarce and good rainfall amount and distribution during growing season. The greatest variation of the cereal yield was observed in manure amended soil (S4) and it was lower and similar in the areas of remaining SICS (S2-S5). The results will help to to select most effective SICS for localized improving crop productivity and adaptation to global warming.</p><p>Acknowledgements.The study was funded by HORIZON 2020, European Commission, Programme H2020-SFS-2015-2: SoilCare for profitable and sustainable crop production in Europe, project No. 677407 (SoilCare, 2016-2021).</p>

The Pharus latifolius Genome Bridges the Gap of Early Grass Evolution

The grass family (Poaceae) includes all commercial cereal crops and is a major contributor to biomass in various terrestrial ecosystems. The ancestry of all grass genomes includes a shared whole-genome duplication (WGD), named rho (ρ) WGD, but the evolutionary significance of ρ-WGD remains elusive. We sequenced the genome of Pharus latifolius, a grass species (producing a true spikelet) in the subfamily Pharoideae, a sister lineage to the core Poaceae including the PACMAD and BOP clades. Our results indicate that the P. latifolius genome has evolved slowly relative to cereal grass genomes, as reflected by moderate rates of molecular evolution, limited chromosome rearrangements and a low rate of gene loss for duplicated genes. We show that the ρ-WGD event occurred ∼98.2 million years ago (Ma) in a common ancestor of the Pharoideae and the PACMAD and BOP grasses. This was followed by contrasting patterns of diploidization in the Pharus and core Poaceae lineages. The presence of two FRIZZY PANICLE (FZP)-like genes in P. latifolius, and duplicated MADS-box genes, support the hypothesis that the ρ-WGD may have played a role in the origin and functional diversification of the spikelet, an adaptation in grasses related directly to cereal yields. The P. latifolius genome sheds light on the origin and early evolution of grasses underpinning the biology and breeding of cereals.