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Flowering time (FT) is the developmental transition coupling an internal genetic program with external local and seasonal climate cues. The genetic loci sensitive to predictable environmental signals underlie local adaptation. We dissected natural variation in FT across a new global diversity set of 473 unique accessions, with >12,000 plants across two seasonal plantings in each of two simulated local climates, Spain and Sweden. Genome-wide association mapping was carried out with 213,497 SNPs. A total of 12 FT candidate quantitative trait loci (QTL) were fine-mapped in two independent studies, including 4 located within ±10 kb of previously cloned FT alleles and 8 novel loci. All QTL show sensitivity to planting season and/or simulated location in a multi-QTL mixed model. Alleles at four QTL were significantly correlated with latitude of origin, implying past selection for faster flowering in southern locations. Finally, maximum seed yield was observed at an optimal FT unique to each season and location, with four FT QTL directly controlling yield. Our results suggest that these major, environmentally sensitive FT QTL play an important role in spatial and temporal adaptation.

Soil-borne plant diseases are increasingly causing devastating losses in agricultural production. The development of a more refined model for disease prediction can aid in reducing crop losses through the use of preventative control measures or soil fallowing for a planting season. The emergence of high-throughput DNA sequencing technology has provided unprecedented insight into the microbial composition of diseased versus healthy soils. However, a single independent case study rarely yields a general conclusion predictive of the disease in a particular soil. Here, we attempt to account for the differences among various studies and plant varieties using a machine-learning approach based on 24 independent bacterial data sets comprising 758 samples and 22 independent fungal data sets comprising 279 samples of healthy or Fusarium wilt-diseased soils from eight different countries. We found that soil bacterial and fungal communities were both clearly separated between diseased and healthy soil samples that originated from six crops across nine countries or regions. Alpha diversity was consistently greater in the fungal community of healthy soils. While diseased soil microbiomes harbored higher abundances of Xanthomonadaceae , Bacillaceae , Gibberella , and Fusarium oxysporum , the healthy soil microbiome contained more Streptomyces Mirabilis , Bradyrhizobiaceae , Comamonadaceae , Mortierella , and nonpathogenic fungi of Fusarium . Furthermore, a random forest method identified 45 bacterial OTUs and 40 fungal OTUs that categorized the health status of the soil with an accuracy >80%. We conclude that these models can be applied to predict the potential for occurrence of F. oxysporum wilt by revealing key biological indicators and features common to the wilt-diseased soil microbiome.

Increased intensity, frequency, and fluctuations of El Niño-Southern Oscillation (ENSO) can affect evapotranspiration and contribute to the timing of rice planting. We used the Penman-Monteith method to estimate evapotranspiration values under the influence of ENSO phases from 1991 to 2022. We also calculated the value of reference evapotranspiration (ETo), actual evapotranspiration (ETa), and crop evapotranspiration (ETc) in the critical phase of rice plants to calculate the value of crop water sufficiency index (Is) and potential yield reduction (RYL) to estimate the onset of rice planting period. The findings reveal that the ETo, ETa, and ETc values in the El Niño phase are 0.77% greater, 1.12% lower, and 0.69% lower, and La Niña is 2.84% lower, 10.98% greater, 7.98% greater compared to the neutral phase, sequentially. Furthermore, the El Niño phase causes the rice planting season to be one dekad later than the neutral phase, and the La Niña phase causes the rice planting season to be two dekads earlier than the neutral phase. In addition, the more robust La Niña phase can cause a more extended rice planting season due to abundant water availability. Further research can be carried out by including rice production results on ENSO impacts.

Spatial Information about rice planting season (RPS) in a wide areas, particularly during periods of El Nino, is important to support an information about the availability of rice continously. Application of remote sensing and geographic information system (GIS) technology can support it's information continuously and accurate. In this study, we attempted to identify the rice planting season during El Nino years of 1997, 2006 and 2015 in the Pringsewu district, Lampung and we compared with meteorological drought index. Spatial information of the RPS obtained through Interpretation of multitemporal Landsat data aquired in 1997, 2006 and 2015 using normalized difference vegetation index (NDVI) and the humidity index. While standardized precipitation index (SPI) is used as a indicator of meteorological drought. This study has shown that the application of remote sensing and GIS could accurately monitor the rice planting season during the periods of El Nino in 1997, 2006 and 2015. The fallow land dominated during the El Nino years and there were no significant difference between years. While drought information based on SPI values showed different results between years of El Nino events. In this paper we also discussed the relationship between distribution of fallow land and meteorological drought in a spatial perspective.

Crop survival and growth requires identification of correlations between appropriate suitable planting season and relevant climatic and environmental characteristics. Climatic and environmental conditions may cause water and heat stress at critical stages of crop development and thus affecting planting suitability. Consequently, this may affect crop yield and productivity. This study assesses the influence of climate and environmental variables on rain-fed sunflower planting season suitability in Tanzania. Data on rainfall, temperature, slope, elevation, soil and land use/or cover were accessed from publicly available sources using Google Earth Engine. This is a cloud-based geospatial computing platform for remote sensed datasets. Tanzania sunflower production calendar of 2022 was adopted to mark the start and end limits of planting across the country. The default climate and environmental parameters from FAO database were used. In addition, Pearson correlation was used to evaluate the relationship between rainfall, temperature over Normalized Difference Vegetation Index (NDVI) from 2000 to 2020 at five-year interval for January-April and June–September, for high and poor suitability season. The results showed that planting suitability of sunflower in Tanzania is driven more by rainfall than temperature. It was revealed that intra-annual planting suitability increases gradually from short to long- rain season and diminishes towards dry season of the year. January-April planting season window showing highest suitability (41.65%), whereas June–September indicating lowest suitability (0.05%). Though, not statistically significant, rainfall and NDVI were positively correlated with r = 0.65 and 0.75 whereas negative correlation existed between temperature and NDVI with r = -− 0.6 and − 0.77. We recommend sunflower subsector interventions that consider appropriate intra-regional and seasonal diversity as an important adaptive mechanism to ensure high sunflower yields.

In institutional economic theory, transaction costs are one of the main focuses of discussion. Transaction costs create inefficiencies in production. Roses are one of the main commodities produced by Batu City. This research aims to identify transaction costs that are borne by cut rose farmers in Batu City. Transaction costs result in higher production costs, thereby reducing farmers’ profits. This study uses a descriptive qualitative approach. Research data was obtained through interview techniques with cut rose farmers. The research results show that there are six types of transaction costs that are borne by farmers. Based on the calculation results, farmer transaction costs for 1 planting season are around IDR 7,040,433.00. These transaction costs result in decreased farmer’s profit of cut rose selling because the production cost raise.

Land use/land cover (LULC) change has serious implications for environment as LULC is directly related to land degradation over a period of time and results in many changes in the environment. Monitoring the locations and distributions of LULC changes is important for establishing links between regulatory actions, policy decisions, and subsequent LULC activities. The normalized difference vegetation index (NDVI) has the potential ability to identify the vegetation features of various eco-regions and provides valuable information as a remote sensing tool in studying vegetation phenology cycles. Similarly, the normalized difference built-up index (NDBI) may be used for quoting built-up land. This study aims to detect the pattern of LULC, NDBI, and NDVI change in Lodhran district, Pakistan, from the Landsat images taken over 40 years, considering four major LULC types as follows: water bodies, built-up area, bare soil, and vegetation. Supervised classification was applied to detect LULC changes observed over Lodhran district as it explains the maximum likelihood algorithm in software ERDAS imagine 15. Most farmers (46.6%) perceived that there have been extreme changes of onset of temperature, planting season, and less precipitation amount in Lodhran district in the last few years. In 2017, building areas increased (4.3%) as compared to 1977. NDVI values for Lodhran district were highest in 1977 (up to + 0.86) and lowest in 1997 (up to − 0.33). Overall accuracy for classification was 86% for 1977, 85% for 1987, 86% for 1997, 88% for 2007, and 95% for 2017. LULC change with soil types, temperature, and NDVI, NDBI, and slope classes was common in the study area, and the conversions of bare soil into vegetation area and built-up area were major changes in the past 40 years in Lodhran district. Lodhran district faces rising temperatures, less irrigation water, and low rainfall. Farmers are aware of these climatic changes and are adapting strategies to cope with the effects but require support from government.

Optimizing the spatial distribution of plants under normal conditions of water and fertilizer is widely used by farmers to improve soybean yield. However, the relationship between soybean yield and spatial plant distribution in the field has not been well studied. This study examined the effect of planting density and plant distribution pattern on soybean plant growth, yield components, canopy light interception, and dry matter accumulation. We also analyzed the relationship between photosynthetic rate, dry matter accumulation, and yield under different planting densities and plant distribution. A two year field experiment was conducted during the 2018 and 2019 soybean planting seasons. Two planting densities (1.8 × 105 and 2.7 × 105 plants ha−1) and two plant distribution patterns (uniform and non-uniform plant spacing) were tested. Higher planting density significantly increased the canopy light interception and dry matter accumulation during soybean growth, leading to increased soybean productivity. The seed yield of soybean under higher planting density was 22.8% higher than under normal planting density. Soybean planted under uniform spacing significantly reduced the differences plant-to-plant. Uniform plant spacing significantly increased the canopy light interception and dry matter accumulation of the soybean population. In addition, the coefficient of variation of seed weight per plant between individual plants under uniform plant distribution decreased by 71.5% compared with non-uniform plant distribution. Furthermore, uniform plant distribution increased soybean seed yield by 9.5% over non-uniform plant distribution. This study demonstrates that increasing planting density under uniform plant distribution can be useful to obtain higher seed yield without increasing other farm inputs.

COVID-19 pandemic movement restrictions as part of the control measures put in place by countries in Sub-Saharan Africa (SSA) has implications on food security, as movement restrictions coincided with planting periods for most of the staple crops. The measures are affecting important staple crops in SSA, and are likely to exacerbate food security challenges in many countries. Achieving adequate food supply in SSA requires developing better policies and packages to confronting the challenge of reducing hunger post COVID-19 pandemic. The lessons learned after COVID-19 crisis will be very important for African countries to rethink their strategies and policies for sustainable economic growth, as COVID-19 many have significant impacts on all sectors of their economies.

Carrot ( Daucus carota L.) is one of the most important root crops grown worldwide and in Ethiopia. However, its production and productivity are low due to a lack of improved varieties and unbalanced fertilizer rates, among other factors. The field experiment was, therefore, conducted to determine the performance of carrot varieties through blended fertilizer rates at Gondar district for a consecutive period of two years. The treatment consisted of six rates of blended NPSB (Nitrogen, phosphorus, sulfur and Boron) fertilizer (0, 40.6, 81.3, 122, 162.3, and 203.4 kg ha -1 ) and two carrot varieties (Haramay-I and Nantes), which were laid out in a randomized complete block design with three replications. The main effect of blended NPSB received in 162.3 kg ha -1 obtained the highest root diameter (3.38 cm), root length (20.93 cm), and root volume (110.60 mm). The main effect of the year was also the maximum number of leaves (10.3), root diameter (2.96 cm), root length (20.09 cm), and root volume (89.20 mm) recorded from the 2021 planting year. On the other hand, in the interaction of variety and NPSB, the highest root fresh weight (134.48 g plant -1 ) was obtained from the Haramaya-I variety and the application of 162.3 NPSB kg ha -1 , while the lowest (57.13 g plant -1 ) was recorded by the Nantes variety with control. The highest dry matter (13.67%), marketable (50.77 t ha -1 ) and total (55.32 t ha -1 ) root yields were recorded from the interaction of 162.3 kg NPSB ha -1 and Haramaya-I variety. Therefore, the planting season and varietal selection should be considered in the carrot production area.