Refining flowering date enhances sesame yield independently of day-length.

BACKGROUND: The transition from vegetative to reproductive growth is a key factor in yield maximization. Sesame (Sesamum indicum), an indeterminate short-day oilseed crop, is rapidly being introduced into new cultivation areas. Thus, decoding its flowering mechanism is necessary to facilitate adaptation to environmental conditions. In the current study, we uncover the effect of day-length on flowering and yield components using F 2 populations segregating for previously identified quantitative trait loci (Si_DTF QTL) confirming these traits. RESULTS: Generally, day-length affected all phenotypic traits, with short-day preceding days to flowering and reducing yield components. Interestingly, the average days to flowering required for yield maximization was 50 to 55 days, regardless of day-length. In addition, we found that Si_DTF QTL is more associated with seed-yield and yield components than with days to flowering. A bulk-segregation analysis was applied to identify additional QTL differing in allele frequencies between early and late flowering under both day-length conditions. Candidate genes mining within the identified major QTL intervals revealed two flowering-related genes with different expression levels between the parental lines, indicating their contribution to sesame flowering regulation. CONCLUSIONS: Our findings demonstrate the essential role of flowering date on yield components and will serve as a basis for future sesame breeding.

Simulating the effects of optimizing sowing date and variety shift on maize production at finer scale in northeast China under future climate.

BACKGROUND: Global warming and the rising occurrences of climate extremes have become formidable challenges for maize production in northeast China. The optimization of sowing date and variety choice stand out as two economic approaches for maize to enhance its resilience to climate change. Nevertheless, assessment of the potential of optimizing sowing date and variety shift on maize yield at finer scale remains underexamined. This study investigated the implications of optimizing sowing date and implementing variety shift on maize yield from a regional perspective. RESULTS: Compared to the reference period (1986-2005), climate change would decrease by 11.5-34.6% (the range describes the differences among climate scenarios and agro-ecological regions) maize yield in the 2050s (2040-2059) if no adaption measure were to be implemented. The combined adaption (optimizing sowing date and variety shift) can improve maize yield by 38.8 ± 11.3%, 42.7 ± 9.7% and 33.9 ± 7.6% under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, respectively. The current sowing window typically falls within the projected optimal sowing window, defined as the period capable of achieving 90% of the maximum yield within the potential sowing window under future climate conditions. Consequently, the potential of the effect of optimizing sowing window on maize yield is limited. In contrast, variety shift results in higher yield improvement, as temperature rise creates favorable conditions for transplanting varieties with an extended growth period, particularly in high latitudes and mountainous regions. Under future climate, cumulative precipitation and compound drought and hot days during maize growing seasons are two key factors influencing maize production. CONCLUSIONS: The optimization of sowing date and variety choice can improve maize yield in northeast China. In addition, maize production should consider varieties with longer growth period and drought and heat tolerance to adapt to climate change. © 2023 Society of Chemical Industry.

Graphical analysis of multi-environmental trials for wheat grain yield based on GGE-biplot analysis under diverse sowing dates.

BACKGROUND: Information on the nature and extent of genetic and genotype × environment (GE) interaction is extremely rare in wheat varieties under different sowing dates. In the present study, the GGE biplot method was conducted to investigate genotype × environment interaction effects and evaluate the adaptability and yield stability of 13 wheat varieties across eight sowing dates, in order to facilitate comparison among varieties and sowing dates and identify suitable varieties for the future breeding studies. RESULTS: Considerable genotypic variation was observed among genotypes for all of the evaluated traits, demonstrating that selection for these traits would be successful. Low broad sense heritability obtained for grain yield showed that, both genetic and non-genetic gene actions played a role in the control of this trait, and suggested that indirect selection based on its components which had high heritability and high correlation with yield, would be more effective to improve grain yield in this germplasm. Hence, selection based on an index may be more useful for improvement of this trait in recurrent selection programs. The results of the stability analysis showed that the environmental effect was a major source of variation, which captured 72.21% of total variation, whereas G and GE explained 6.94% and 18.33%, respectively. The partitioning of GGE through GGE biplot analysis showed that, the first two PCs accounted for 54.64% and 35.15% of the GGE sum of squares respectively, capturing a total of 89.79% variation. According to the GGE biplot, among the studied varieties, the performance of Gascogen was the least stable, whereas Sirvan, Roshan, and Pishtaz had superior performance under all sowing dates, suggesting that they have a broad adaptation to the diverse sowing dates. These varieties may be recommended for genetic improvement of wheat with a high degree of adaptation. CONCLUSION: The results obtained in this study demonstrated the efficiency of the GGE biplot technique for selecting high yielding and stable varieties across sowing dates.

Optimization of sowing date for spring maize in China's loess plateau based on presowing temperature and soil water content.

BACKGROUND: The sowing date of spring maize in China's Loess Plateau is often restricted by the presowing temperature and soil water content (SWC). The effective measurement of the effects of presowing temperature and SWC on the sowing date is a major concern for agricultural production in this region. In this paper, we considered the average daily air temperature of ˃10 °C in the 7 days before sowing. The Decision Support System for Agrotechnology Transfer (DSSAT) model was used to simulate a spring maize yield under distinct combinations of SWC and sowing date for 51 years (1970-2020). Subsequently, through the cumulative probability distribution function, the contribution of presowing SWC to the spring maize yield was quantified, and the optimal sowing date of spring maize in each production location was determined. RESULTS: The results revealed that the daily average temperature of ˃10 °C for 7 days consecutively can be used effectively as the basis for the simulation of spring maize sowing date. The presowing SWC affected the spring maize yield but did not change the optimal sowing date. When the SWC of each study site is about 70% of the field capacity, Wenshui and Yuanping can properly delay sowing, and Lin county can sow early to obtain a higher yield. CONCLUSION: This study provides an effective approach for optimizing presowing soil moisture management and the sowing date of spring maize in the semiarid regions of the Loess Plateau. © 2023 Society of Chemical Industry.

[Effect of sowing dates on physiological characteristics, yield, and quality of Carthamus tinctorius].

A field experiment was conducted to measure the physiological characteristics, yield, active ingredient content, and other indicators of Carthamus tinctorius leaves undergoing 13 sowing date treatments. The principal component analysis(PCA) and redundancy analysis were used to analyze the correlation between these indicators to explore the effect of sowing date on the yield and active ingredient content of C. tinctorius in Liupanshan of Ningxia. The results illustrated that the early sowing in autumn and spring had significant effects on leaf photosynthetic parameters, SPAD value, antioxidant enzyme activity, nitrogen metabolism enzyme activity, filament yield, grain yield, and hydroxy safflower yellow A(HYSA) of C. tinctorius. Sowing in mid-November and late March had the best effect. Leaf transpiration rate, stomatal conductance, nitrate reductase, nitrite reductase, glutamine synthetase, and glutamate synthase increased by 44.9%, 52.4%, 15.9%, 60.8%, 10.3%, and 38.3%, respectively. The activities of superoxide dismutase, peroxidase, and catalase decreased by 10.8%, 4.1%, and 20.9%, respectively. The improvement of photosynthetic physiological characteristics promoted the dry matter accumulation and reproductive growth of C. tinctorius. The yield of filaments and seeds increased by 15.5% and 11.7%, and the yield of HYSA and kaempferol increased by 17.9% and 20.0%. In short, the suitable sowing date can promote the growth and development of C. tinctorius in Liupanshan of Ningxia, and significantly improve the yield and quality, which is conducive to the high quality and efficient production of C. tinctorius.

Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment.

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.

Modelling the effects of cold temperature during the reproductive stage on the yield of chickpea (Cicer arietinum L.).

During the reproductive stage, chilling temperatures and frost reduce the yield of chickpea and limit its adaptation. The adverse effects of chilling temperature and frost in terms of the threshold temperatures, impact of cold duration, and genotype-by-environment-by-management interactions are not well quantified. Crop growth models that predict flowering time and yield under diverse climates can identify combinations of cultivars and sowing time to reduce frost risk in target environments. The Agricultural Production Systems Simulator (APSIM-chickpea) model uses daily temperatures to model basic crop growth but does not include penalties for either frost damage or cold temperatures during flowering and podding stages. Regression analysis overcame this limitation of the model for chickpea crops grown at 95 locations in Australia using 70 years of historic data incorporating three cultivars and three sowing times (early, mid, and late). We modified model parameters to include the effect of soil water on thermal time calculations, which significantly improved the prediction of flowering time. Simulated data, and data from field experiments grown in Australia (2013 to 2019), showed robust predictions for flowering time (n = 29; R2 = 0.97), and grain yield (n = 22; R2 = 0.63-0.70). In addition, we identified threshold cold temperatures that significantly affected predicted yield, and combinations of locations, variety, and sowing time where the overlap between peak cold temperatures and peak flowering was minimal. Our results showed that frost and/or cold temperature-induced yield losses are a major limitation in some unexpected Australian locations, e.g., inland, subtropical latitudes in Queensland. Intermediate sowing maximise yield, as it avoids cold temperature, late heat, and drought stresses potentially limiting yield in early and late sowing respectively.

Variation of floret development and grain setting characteristics in winter wheat responses to delayed sowing.

BACKGROUND: Wheat floret development has been a focus of research due to a desire to improve spike fertility, which majorly influences grain yield. Sowing date plays a vital role on grain yield in wheat, and increase in the grain number per spike of winter wheat (Triticum aestivum L.) has been obtained by delayed sowing. During the 2014-2015 and 2015-2016 growing seasons, variation in these developmental patterns was explored involving two winter wheat cultivars (Jimai 22 and Tainong 18) and five sowing dates (24 September; 1, 8, 15 and 22 October). RESULTS: We noticed clear differences in the grain number per spikelet; delayed sowing had a greater impact on the number of fertile florets at anthesis than grain set. Significant differences in the developmental patterns of florets among spikelet positions corresponded to variations in the floret developmental rate, with faster floret development associated with higher floret fertility. Delayed sowing did not affect the grain number near the rachis, but significantly promoted grain set on distal florets. Increased spike dry weight (SDW) did not compensate for floret size or grain weight, mainly due to enhanced assimilate partitioning to florets. CONCLUSION: Delayed sowing significantly affects floret developmental dynamics, causing differences in winter wheat floret fertility. An increased SDW concomitant with improved intra-spike partitioning before anthesis contributes to increase the distal floret numbers per spike and then optimize winter wheat spike fertility. © 2022 Society of Chemical Industry.

Optimized seeding rate and nitrogen topdressing ratio for simultaneous improvement of grain yield and bread-making quality in bread wheat sown on different dates.

BACKGROUND: Sowing date, seeding rate, and nitrogen (N) topdressing ratio have strong effects on grain yield (GY) and bread-making quality (BQ) in bread wheat. Simultaneous improvement in GY and BQ in bread wheat has long been a challenge due to the inverse relationship between GY and grain protein concentration (GPC). In this study, we investigated whether the GY and BQ of bread wheat sown on different dates could be improved simultaneously by optimizing the seeding rate and the N topdressing ratio. RESULTS: Delaying sowing beyond a certain period led to decreases in both GY and BQ. Optimizing the seeding rate and N topdressing ratio enhanced the N uptake during pre- and post-anthesis, as well as N remobilization during grain filling for all wheat plants sown on different dates, thereby increasing the GPC and the total N per grain (Ntot ). Consequently, grain protein composition was improved, resulting in an increased glutenin/gliadin ratio, sodium dodecyl sulfate-insoluble glutenin/total glutenin (i.e., glutenin polymerization index), and high-molecular-weight glutenin subunit/ low-molecular-weight glutenin subunit (HMW-GS/LMW-GS) ratio. Increased GPC and improved grain protein composition enhanced BQ. CONCLUSION: The mechanism underlying simultaneous improvement in GY and GPC as well as Ntot was the greater increase in N accumulation in grains per unit area relative to increases in GY, or total grain number per unit area. The GY and BQ can be improved simultaneously regardless of sowing date by optimizing the seeding rate and N topdressing ratio via enhanced N uptake and N remobilization into grains. © 2021 Society of Chemical Industry.

Optimization of timing of next-generation emergence in Amaranthus hybridus is determined via modulation of seed dormancy by the maternal environment.

The timing of emergence of weed species has critical ecological and agronomical implications. In several species, emergence patterns largely depend on the level of dormancy of the seedbank, which is modulated by specific environmental factors. In addition, environmental conditions during seed maturation on the mother plant can have marked effects on the dormancy level at the time of seed dispersal. Hence, the maternal environment has been suggested to affect seedbank dormancy dynamics and subsequent emergence; however, this modulation has not been adequately examined under field conditions, and the mechanisms involved are only partly understood. Combining laboratory and field experiments with population-based models, we investigated how dormancy level and emergence in the field are affected by the sowing date and photoperiod experienced by the mother plant in Amaranthus hybridus, a troublesome weed worldwide. The results showed that an earlier sowing date and a longer photoperiod enhanced the level of dormancy by increasing the dormancy imposed by both the embryo and the seed coat. However, this did not affect the timing and extent of emergence in the field; on the contrary, the variations in dormancy level contributed to synchronizing the emergence of the next generation of plants with the time period that maximized population fitness. Our results largely correspond with effects previously observed in other species such as Polygonum aviculare and Arabidopsis, suggesting a common effect exists within different species.

Genetically modified maize hybrids and delayed sowing reduced drought effects across a rainfall gradient in temperate Argentina.

Before the introduction of genetically modified insect-tolerant maize (Zea mays L.) in 1997, most of the production of this staple in Argentina was concentrated in humid and sub-humid temperate regions. Early spring sowings minimized the risk of water deficit around flowering and yield reduction due to pests. Use of genetically modified maize allowed optimization of sowing dates to synchronize critical periods for kernel set determination with the times of the year when water deficits are less likely, reducing large interannual variations in grain yield. This change in sowing date did not start until 2009, after the occurrence of two successive dry phases of the El Niño-Southern Oscillation phenomenon. The area of land cropped to maize in Argentina has expanded dramatically since then, particularly beyond the humid areas. Currently, maize is sown in an almost 50%/50% distribution between early and late sowings, including double cropping. Changes in agronomic practices such as sowing date and production area can lead to changes in the timing and intensity of water deficits along the maize growth cycle. This review provides an overview of new patterns of water deficit across humid, sub-humid, and semi-arid mid-latitude environments of Argentina, and their effects on grain yield and yield components.

The use of remote sensing to derive maize sowing dates for large-scale crop yield simulations.

One of the major sources of uncertainty in large-scale crop modeling is the lack of information capturing the spatiotemporal variability of crop sowing dates. Remote sensing can contribute to reducing such uncertainties by providing essential spatial and temporal information to crop models and improving the accuracy of yield predictions. However, little is known about the impacts of the differences in crop sowing dates estimated by using remote sensing (RS) and other established methods, the uncertainties introduced by the thresholds used in these methods, and the sensitivity of simulated crop yields to these uncertainties in crop sowing dates. In the present study, we performed a systematic sensitivity analysis using various scenarios. The LINTUL-5 crop model implemented in the SIMPLACE modeling platform was applied during the period 2001-2016 to simulate maize yields across four provinces in South Africa using previously defined scenarios of sowing dates. As expected, the selected methodology and the selected threshold considerably influenced the estimated sowing dates (up to 51 days) and resulted in differences in the long-term mean maize yield reaching up to 1.7 t ha-1 (48% of the mean yield) at the province level. Using RS-derived sowing date estimations resulted in a better representation of the yield variability in space and time since the use of RS information not only relies on precipitation but also captures the impacts of socioeconomic factors on the sowing decision, particularly for smallholder farmers. The model was not able to reproduce the observed yield anomalies in Free State (Pearson correlation coefficient: 0.16 to 0.23) and Mpumalanga (Pearson correlation coefficient: 0.11 to 0.18) in South Africa when using fixed and precipitation rule-based sowing date estimations. Further research with high-resolution climate and soil data and ground-based observations is required to better understand the sources of the uncertainties in RS information and to test whether the results presented herein can be generalized among crop models with different levels of complexity and across distinct field crops.

Social-ecological analysis of timely rice planting in Eastern India.

Timely crop planting is a foundation for climate-resilient rice-wheat systems of the Eastern Gangetic Plains-a global food insecurity and poverty hotspot. We hypothesize that the capacity of individual farmers to plant on time varies considerably, shaped by multifaceted enabling factors and constraints that are poorly understood. To address this knowledge gap, two complementary datasets were used to characterize drivers and decision processes that govern the timing of rice planting in this region. The first dataset was a large agricultural management survey (rice-wheat: n = 15,245; of which rice: n = 7597) from a broad geographic region that was analyzed by machine learning methods. The second dataset was a discussion-based survey (n = 112) from a more limited geography that we analyzed with graph theory tools to elicit nuanced information on planting decisions. By combining insights from these methods, we show for the first time that differences in rice planting times are primarily shaped by ecosystem and climate factors while social factors play a prominent secondary role. Monsoon onset, surface and groundwater availability, and land type determine village-scale mean planting times whereas, for resource-constrained farmers who tend to plant later ceteris paribus, planting is further influenced by access to farm machinery, seed, fertilizer, and labor. Also, a critical threshold for economically efficient pumping appears at a groundwater depth of around 4.5 m; below this depth, farmers do not irrigate and delay planting. Without collective action to spread risk through synchronous timely planting, ecosystem factors such as threats posed by pests and wild animals may further deter early planting by individual farmers. Accordingly, we propose a three-pronged strategy that combines targeted strengthening of agricultural input chains, agroadvisory development, and coordinated rice planting and wildlife conservation to support climate-resilient agricultural development in the Eastern Gangetic Plains.

Regulation of seed dormancy by the maternal environment is instrumental for maximizing plant fitness in Polygonum aviculare.

Emergence at an appropriate time and place is critical for maximizing plant fitness and hence sophisticated mechanisms such as seed dormancy have evolved. Although maternal influence on different aspects of dormancy behavior has been identified, its impact under field conditions and its relation to plant fitness has not been fully determined. This study examined maternal effects in Polygonum aviculare on release of seed primary dormancy, responses to alternating temperatures, induction into secondary dormancy, and field emergence patterns as influenced by changes in the sowing date and photoperiod experienced by the mother plant. Maternal effects were quantified using population threshold models that allowed us to simulate and interpret the experimental results. We found that regulation of dormancy in P. aviculare seeds by the maternal environment is instrumental for maximizing plant fitness in the field. This regulation operates by changing the dormancy level of seeds dispersed at different times (as a consequence of differences in the sowing dates of mother plants) in order to synchronize most emergence to the seasonal period that ultimately guarantees the highest reproductive output of the new generation. Our results also showed that maternal photoperiod, which represents a clear seasonal cue, is involved in this regulation.

Risk assessment of frost damage to sugar beet simulated under cold and semi-arid environments.

In the semi-arid climatic conditions, water shortage is a key factor to generate crop production. Planting in autumn and winter and using precipitation can help cope with the problem. But in the semi-arid areas with cold winter, frost is another limited factor affecting crop production. For this purpose, in the present study, a simple and universal crop growth simulator (SUCROS) model was used to estimate the potential yield of sugar beets and frost damage from 1993 to 2009 for four autumn sowing dates (2 October, 17 October, 1 November, and 16 November) and two spring dates (6 March and 6 May) in eight locations (Birjand, Bojnord, Ghaen, Mashhad, Torbat-e Heydarieh, Neyshabor, Torbat-e Jam, and Ghochan) of the Khorasan province in northeastern Iran as a semi-arid and cold area. There was a large variability between locations and years in terms of frost damage. The crop failure from frost for the autumn sowing dates ranged from 62.5 to 100% at Neyshabor and Ghochan, respectively. Although autumn sowing dates performed better than spring sowing dates in terms of fresh storage organ yield (~ 109.9 t ha-1 vs. ~ 78.4 t ha-1), the risk of frost stress under autumn sowing dates was high at all studied locations. To maximize potential yield and minimize frost risk, sugar beet farmers under semi-arid and frost-prone conditions in the world such as Khorasan province should choose optimum sowing dates outside the high frost risk period to avoid crop damage. The last frost day under these areas normally happened between the 15th and 28th of February, after which no frost events occurred. Accordingly, it is recommended to farmers to sow sugar beet after the period during which no frost risk for sugar beet occurred.

Impact of warming climate, sowing date, and cultivar shift on rice phenology across China during 1981-2010.

Climate change would exert significantly impact on crop yield by altering crop growth and development processes. Therefore, to ensure food security, it is necessary to assess the response and adaptation of crop phenology to the natural (mainly climate change) and artificial (including sowing date (SD) change and cultivar shift) factors. In this study, using field data from 113 agro-meteorological experiment stations across China, along with the Agricultural Production System Simulator (APSIM) oryza model, we investigated the trends of rice phenology in relation to climate change and agronomic factors (i.e., SD change and cultivar shift) from 1981 to 2010. We found that flowering date (FD) and maturity date (MD) of single-rice were delayed by 0.3 and 1.4 days 10a-1, respectively, but FD and MD of double-rice were advanced by 0.7-0.8 and 0.2-1.1 days 10a-1, respectively. Climate change advanced FD and MD of rice at representative stations except FD of late-rice, and shortened length of rice growth period. SD change of rice mainly affected duration of vegetative growth phase (VGP, from SD to FD), but had no significant impact on duration of reproductive growth phase (RGP, from FD to MD). Cultivar shift delayed FD and MD of rice at all representative stations except late-rice at Lianhua. Moreover, cultivar shift prolonged the duration of rice RGP by 0.2-2.8 days 10a-1. Overall, the results suggested that rice phenology was adapting to ongoing climate change by SD change and adoption of cultivars with longer RGP. Therefore, crop phenological characteristics should be sufficiently taken into account to develop adaptation strategies in the future.

Temporal and Spectral Optimization of Vegetation Indices for Estimating Grain Nitrogen Uptake and Late-Seasonal Nitrogen Traits in Wheat.

Grain nitrogen (N) uptake (GNup) in winter wheat (Triticum aestivum L.) is influenced by multiple components at the plant organ level and by pre- and post-flowering N uptake (Nup). Although spectral proximal high-throughput sensing is promising for field phenotyping, it was rarely evaluated for such N traits. Hence, 48 spectral vegetation indices (SVIs) were evaluated on 10 measurement days for the estimation of 34 N traits in four data subsets, representing the variation generated by six high-yielding cultivars, two N fertilization levels (N), two sowing dates (SD), and two fungicide (F) intensities. Close linear relationships (p < 0.001) were found for GNup both in response to cultivar differences (Cv; R2 = 0.52) and other agronomic treatments (R2 = 0.67 for Cv*F*N, R2 = 0.53 for Cv*SD*N and R2 = 0.57 for the combined treatments), notably during milk ripeness. Especially near-infrared (NIR)/red edge SVIs, such as the NDRE_770_750, outperformed NIR/visible light (VIS) indices. Index rankings and seasonal R2 values were similar for total Nup, while the N harvest index, which expresses the partitioning to the grain, was moderately estimated only during dough ripeness, primarily from indices detecting contrasting senescence between different fungicide intensities. Senescence-sensitive indices, including R787_R765 and TRCARI_OSAVI, performed best for N translocation efficiency and some organ-level N traits at maturity. Even though grain N concentration was best assessed by the red edge inflection point (REIP), the blue/green index (BGI) was more suited for leaf-level N traits at anthesis. When SVIs were quantitatively ranked by data subsets, a better agreement was found for GNup, total Nup, and grain N concentration than for several contributing N traits. The results suggest (i) a good general potential for estimating GNup and total Nup by (ii) red edge indices best used (iii) during milk and early dough ripeness. The estimation of contributing N traits differs according to the agronomic treatment.

Mapping of Rice Varieties and Sowing Date Using X-Band SAR Data.

Rice is a major staple food for nearly half of the world's population and has a considerable contribution to the global agricultural economy. While spaceborne Synthetic Aperture Radar (SAR) data have proved to have great potential to provide rice cultivation area, few studies have been performed to provide practical information that meets the user requirements. In rice growing regions where the inter-field crop calendar is not uniform such as in the Mekong Delta in Vietnam, knowledge of the start of season on a field basis, along with the planted rice varieties, is very important for correct field management (timing of irrigation, fertilization, chemical treatment, harvest), and for market assessment of the rice production. The objective of this study is to develop methods using SAR data to retrieve in addition to the rice grown area, the sowing date, and the distinction between long and short cycle varieties. This study makes use of X-band SAR data from COSMO-SkyMed acquired from 19 August to 23 November 2013 covering the Chau Thanh and Thoai Son districts in An Giang province, Viet Nam, characterized by a complex cropping pattern. The SAR data have been analyzed as a function of rice parameters, and the temporal and polarization behaviors of the radar backscatter of different rice varieties have been interpreted physically. New backscatter indicators for the detection of rice paddy area, the estimation of the sowing date, and the mapping of the short cycle and long cycle rice varieties have been developed and assessed. Good accuracy has been found with 92% in rice grown area, 96% on rice long or short cycle, and a root mean square error of 4.3 days in sowing date. The results have been discussed regarding the generality of the methods with respect to the rice cultural practices and the SAR data characteristics.

Evaluation of the productivity of maize (ATP) under different sowing dates in the western highlands of Cameroon.

This study aims to assess the effects of three sowing dates on the growth and yield of maize. The trial was conducted at the Teaching and Research Farm of the University of Dschang in Bansoa (Penka-Michel) over two growth cycles: 2020 and 2021. The experiment was arranged in a randomized complete block design. Treatments, replicated three times, included a control (T) and three levels of fertilizer input: mineral fertilizer (E), organic fertilizer (F), and organo-mineral fertilizer (EF). The maize variety used was the Acid Tolerable Population (ATP). Sowing occurred sequentially on March 30, April 10, and April 20. Daily rainfall data were collected using a graduated cylinder rain gauge. Observations focused on growth and yield parameters at different sowing dates. The results obtained indicate that maize responded significantly to different sowing dates (p < 0.05). Fertilizer application also positively influenced growth compared to the control. The tallest plants were observed in the F treatment sown during the first date (300 cm). Similarly, the collar diameter followed the same trend, with the F treatment measuring 3.43 cm compared to 2.14 cm for the control, indicating significant differences. Yield results showed the first sowing date > second date > third date. The highest yield was achieved with the EF treatment during the first period (6985 kg/ha), compared to the control during the third period (2009 kg/ha). Within each period, the yield results followed the order: EF > E > F > T. Organic fertilizers alone also produced favorable results compared to the control. Given the current climatic conditions and the results obtained, maize sowing conducted in the first sowing period (March 30) with organo-mineral fertilizer would likely result in optimal grain yield.

Influence of climatic variables on maize grain yield and its components by adjusting the sowing date.

Yield and its components are greatly affected by climate change. Adjusting the sowing date is an effective way to alleviate adverse effects and adapt to climate change. Aiming to determine the optimal sowing date of summer maize and clarify the contribution of climatic variables to grain yield and its components, a consecutive 4-year field experiment was conducted from 2016 to 2019 with four sowing dates at 10-day intervals from 5 June to 5 July. Analysis of historical meteorological data showed that more solar radiation (SR) was distributed from early June to mid-August, and the maximum temperature (Tmax) > 32°C appeared from early July to late August, which advanced and lasted longer in 1991-2020 relative to 1981-1990. Additionally, the precipitation was mainly distributed from early June to late July. The climate change in the growing season of summer maize resulted in optimal sowing dates ranging from 5 June to 15 June, with higher yields and yield stability, mainly because of the higher kernel number per ear and 1,000-grain weight. The average contribution of kernel number per ear to grain yield was 58.7%, higher than that of 1,000-grain weight (41.3%). Variance partitioning analysis showed that SR in 15 days pre-silking to 15 days post-silking (SS) and silking to harvest (SH) stages significantly contributed to grain yield by 63.1% and 86.4%. The extreme growing degree days (EDD) > 32°C, SR, precipitation, and diurnal temperature range (DTR) contributed 20.6%, 22.9%, 14.5%, and 42.0% to kernel number per ear in the SS stage, respectively. Therefore, we concluded that the early sowing dates could gain high yield and yield stability due to the higher SR in the growing season. Meanwhile, due to the decreasing trend in SR and increasing Tmax trend in this region, in the future, new maize varieties with high-temperature resistance, high light efficiency, shade tolerance, and medium-season traits need to be bred to adapt to climate change and increased grain yield.